%%% -*-BibTeX-*-
%%% ====================================================================
%%% BibTeX-file{
%%% author = "Nelson H. F. Beebe",
%%% version = "1.166",
%%% date = "28 August 2024",
%%% time = "09:51:09 MDT",
%%% filename = "pagerank.bib",
%%% address = "University of Utah
%%% Department of Mathematics, 110 LCB
%%% 155 S 1400 E RM 233
%%% Salt Lake City, UT 84112-0090
%%% USA",
%%% telephone = "+1 801 581 5254",
%%% FAX = "+1 801 581 4148",
%%% URL = "https://www.math.utah.edu/~beebe",
%%% checksum = "65414 19092 90667 905770",
%%% email = "beebe at math.utah.edu, beebe at acm.org,
%%% beebe at computer.org (Internet)",
%%% codetable = "ISO/ASCII",
%%% keywords = "AuthorRank; BadRank; BookRank; BuddyRank;
%%% CiteRank; DiffusionRank; DirRank; FactRank;
%%% FolkRank; GeneRank; GroupRank; HostRank;
%%% IsoRank; ItemRank; LambdaRank; MonitorRank;
%%% ObjectRank; PageRank; PopRank; ProteinRank;
%%% TimedPageRank; TrustRank; TwitterRank;
%%% VisualRank; Web information; Web search;
%%% retrieval",
%%% license = "public domain",
%%% supported = "yes",
%%% docstring = "This is a bibliography of publications
%%% about the Google Brin/Page PageRank
%%% algorithm, and its historical background.
%%% The algorithm is at the core of text
%%% searching done by Google and other
%%% Web-indexing companies.
%%%
%%% At version 1.166, the year coverage looked
%%% like this:
%%%
%%% 1941 ( 1) 1970 ( 0) 1999 ( 1)
%%% 1943 ( 0) 1972 ( 0) 2001 ( 8)
%%% 1944 ( 0) 1973 ( 0) 2002 ( 14)
%%% 1945 ( 0) 1974 ( 0) 2003 ( 26)
%%% 1946 ( 0) 1975 ( 0) 2004 ( 30)
%%% 1947 ( 0) 1976 ( 0) 2005 ( 53)
%%% 1948 ( 0) 1977 ( 0) 2006 ( 71)
%%% 1949 ( 0) 1978 ( 0) 2007 ( 99)
%%% 1950 ( 0) 1979 ( 0) 2008 ( 62)
%%% 1951 ( 0) 1980 ( 0) 2009 ( 48)
%%% 1952 ( 0) 1981 ( 0) 2010 ( 37)
%%% 1953 ( 0) 1982 ( 0) 2011 ( 22)
%%% 1954 ( 0) 1983 ( 0) 2012 ( 23)
%%% 1955 ( 0) 1984 ( 0) 2013 ( 10)
%%% 1956 ( 0) 1985 ( 0) 2014 ( 13)
%%% 1957 ( 0) 1986 ( 0) 2015 ( 16)
%%% 1958 ( 0) 1987 ( 0) 2016 ( 5)
%%% 1959 ( 0) 1988 ( 0) 2017 ( 14)
%%% 1960 ( 0) 1989 ( 0) 2018 ( 14)
%%% 1961 ( 0) 1990 ( 0) 2019 ( 15)
%%% 1962 ( 0) 1991 ( 0) 2020 ( 11)
%%% 1963 ( 0) 1992 ( 0) 2021 ( 11)
%%% 1964 ( 0) 1993 ( 0) 2022 ( 9)
%%% 1965 ( 0) 1994 ( 0) 2023 ( 12)
%%% 1966 ( 0) 1995 ( 0) 2024 ( 5)
%%% 1967 ( 0) 1996 ( 0) 2025 ( 1)
%%% 1968 ( 0) 1997 ( 1)
%%% 1969 ( 0) 1998 ( 2)
%%%
%%% Article: 278
%%% Book: 20
%%% InBook: 4
%%% InCollection: 6
%%% InProceedings: 224
%%% MastersThesis: 1
%%% Misc: 7
%%% PhdThesis: 2
%%% Proceedings: 81
%%% TechReport: 11
%%%
%%% Total entries: 634
%%%
%%% The checksum field above contains a CRC-16
%%% checksum as the first value, followed by the
%%% equivalent of the standard UNIX wc (word
%%% count) utility output of lines, words, and
%%% characters. This is produced by Robert
%%% Solovay's checksum utility.",
%%% }
%%% ====================================================================
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}
%%% ====================================================================
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@String{inst-MATHWORKS:adr = "3 Apple Hill Drive, Natick, MA 01760-2098,
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%%% ====================================================================
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of IMACS"}
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@String{j-BIT = "BIT (Nordisk tidskrift for
informationsbehandling)"}
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@String{j-CACM = "Communications of the ACM"}
@String{j-CCPE = "Concurrency and Computation: Prac\-tice and
Experience"}
@String{j-COMP-J = "The Computer Journal"}
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1999)"}
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Algorithms"}
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Analysis (ETNA)"}
@String{j-FUND-INFO = "Fundamenta Informaticae"}
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bifurcation and chaos in applied sciences
and engineering"}
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Programming"}
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Information Science and Technology: JASIST"}
@String{j-J-ASSOC-INF-SCI-TECHNOL = "Journal of the Association for Information
Science and Technology"}
@String{j-J-COMPUT-APPL-MATH = "Journal of Computational and Applied
Mathematics"}
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@String{j-J-GRID-COMP = "Journal of Grid Computing"}
@String{j-J-INFORMETRICS = "Journal of Informetrics"}
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Computing"}
@String{j-J-PHYS-A-MATH-THEOR = "Journal of Physics A: Mathematical and
Theoretical"}
@String{j-J-R-STAT-SOC-SER-B-STAT-METHODOL = "Journal of the Royal
Statistical Society. Series B
(Statistical Methodology)"}
@String{j-J-SCI-COMPUT = "Journal of Scientific Computing"}
@String{j-J-STAT-MECH-THEORY-EXP = "Journal of Statistical Mechanics: Theory and
Experiment"}
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@String{j-J-SUPERCOMPUTING = "The Journal of Supercomputing"}
@String{j-J-SYST-SOFTW = "The Journal of Systems and Software"}
@String{j-LECT-NOTES-COMP-SCI = "Lecture Notes in Computer Science"}
@String{j-LIN-MULT-ALGEBRA = "Linear Multilinear Algebra"}
@String{j-LINEAR-ALGEBRA-APPL = "Linear Algebra and its Applications"}
@String{j-MATH-COMPUT = "Mathematics of Computation"}
@String{j-MATH-COMPUT-SCI = "Mathematics in Computer Science"}
@String{j-MATH-INTEL = "The Mathematical Intelligencer"}
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Optimization"}
@String{j-NUMER-ALGORITHMS = "Numerical Algorithms"}
@String{j-PHYS-REV-E = "Physical Review E (Statistical physics,
plasmas, fluids, and related
interdisciplinary topics)"}
@String{j-PHYS-TODAY = "Physics Today"}
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@String{j-PROC-IEEE = "Proceedings of the IEEE"}
@String{j-PROC-NATL-ACAD-SCI-USA = "Proceedings of the National Academy of
Sciences of the United States of America"}
@String{j-PROC-VLDB-ENDOWMENT = "Proceedings of the VLDB Endowment"}
@String{j-SCIENTOMETRICS = "Scientometrics"}
@String{j-SIAM-J-MAT-ANA-APPL = "SIAM Journal on Matrix Analysis and
Applications"}
@String{j-SIAM-J-NUMER-ANAL = "SIAM Journal on Numerical Analysis"}
@String{j-SIAM-J-SCI-COMP = "SIAM Journal on Scientific Computing"}
@String{j-SIAM-REVIEW = "SIAM Review"}
@String{j-SIGMETRICS = "ACM SIGMETRICS Performance Evaluation
Review"}
@String{j-STOCH-MODELS = "Stochastic Models"}
@String{j-TACO = "ACM Transactions on Architecture and
Code Optimization"}
@String{j-TALLIP = "ACM Transactions on Asian and Low-Resource
Language Information Processing (TALLIP)"}
@String{j-TCBB = "IEEE\slash ACM Transactions on Computational
Biology and Bioinformatics"}
@String{j-THEOR-COMP-SCI = "Theoretical Computer Science"}
@String{j-THEOR-INFORM-APPL = "Theoretical Informatics and Applications.
Informatique Th{\'e}orique et Applications"}
@String{j-TIST = "ACM Transactions on Intelligent Systems and
Technology (TIST)"}
@String{j-TKDD = "ACM Transactions on Knowledge
Discovery from Data (TKDD)"}
@String{j-TMIS = "ACM Transactions on Management Information
Systems (TMIS)"}
@String{j-TODS = "ACM Transactions on Database Systems"}
@String{j-TOIS = "ACM Transactions on Information Systems"}
@String{j-TOIT = "ACM Transactions on Internet Technology
(TOIT)"}
@String{j-TOMCCAP = "ACM Transactions on Multimedia Computing,
Communications, and Applications"}
@String{j-TOMPECS = "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)"}
@String{j-TOPC = "ACM Transactions on Parallel Computing
(TOPC)"}
@String{j-TOSEM = "ACM Transactions on Software Engineering and
Methodology"}
@String{j-TRETS = "ACM Transactions on Reconfigurable Technology
and Systems (TRETS)"}
@String{j-TWEB = "ACM Transactions on the Web (TWEB)"}
@String{j-VLDB-J = "VLDB Journal: Very Large Data Bases"}
@String{j-WIRED = "Wired"}
%%% ====================================================================
%%% Publisher abbreviations:
@String{pub-ACM = "ACM Press"}
@String{pub-ACM:adr = "New York, NY 10036, USA"}
@String{pub-CAMBRIDGE = "Cambridge University Press"}
@String{pub-CAMBRIDGE:adr = "Cambridge, UK"}
@String{pub-ELSEVIER = "Elsevier"}
@String{pub-ELSEVIER:adr = "Amsterdam, The Netherlands"}
@String{pub-HARVARD = "Harvard University Press"}
@String{pub-HARVARD:adr = "Cambridge, MA, USA"}
@String{pub-IEEE = "IEEE Computer Society Press"}
@String{pub-IEEE:adr = "1109 Spring Street, Suite 300,
Silver Spring, MD 20910, USA"}
@String{pub-IOS = "IOS Press"}
@String{pub-IOS:adr = "Amsterdam, The Netherlands"}
@String{pub-MORGAN-KAUFMANN = "Morgan Kaufmann Publishers"}
@String{pub-MORGAN-KAUFMANN:adr = "Los Altos, CA 94022, USA"}
@String{pub-PRINCETON = "Princeton University Press"}
@String{pub-PRINCETON:adr = "Princeton, NJ, USA"}
@String{pub-QUE = "Que Corporation"}
@String{pub-QUE:adr = "Indianapolis, IN, USA"}
@String{pub-SAS = "SAS Institute"}
@String{pub-SAS:adr = "SAS Circle, Box 8000, Cary, NC
27512-8000, USA"}
@String{pub-SIAM = "Society for Industrial and Applied
Mathematics"}
@String{pub-SIAM:adr = "Philadelphia, PA, USA"}
@String{pub-SV = "Springer-Verlag"}
@String{pub-SV:adr = "Berlin, Germany~/ Heidelberg, Germany~/
London, UK~/ etc."}
@String{pub-WILEY = "Wiley"}
@String{pub-WILEY:adr = "New York, NY, USA"}
%%% ====================================================================
%%% Series abbreviations:
@String{ser-LNAI = "Lecture Notes in Artificial Intelligence"}
@String{ser-LNCIS = "Lecture Notes in Control and Information
Science"}
@String{ser-LNCS = "Lecture Notes in Computer Science"}
%%% ====================================================================
%%% Bibliography entries, sorted by year, and within years, by citation
%%% label, using ``bibsort -byyear''.
@Book{Leontief:1941:SAE,
author = "Wassily W. Leontief",
title = "The Structure of {American} Economy, 1919--1929: an
empirical application of equilibrium analysis",
publisher = pub-HARVARD,
address = pub-HARVARD:adr,
pages = "xi + 181",
year = "1941",
LCCN = "????",
bibdate = "Fri Feb 19 15:19:39 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://nobelprize.org/nobel_prizes/economics/laureates/1973/",
acknowledgement = ack-nhfb,
remark = "The author was awarded the Nobel Prize in Economics in
1973. Franceschet \cite{Franceschet:2010:PSS} traces
the PageRank algorithm back to this book.",
}
@Article{Marchiori:1997:QCI,
author = "Massimo Marchiori",
title = "The quest for correct information on the {Web}: Hyper
search engines",
journal = j-COMP-NET-ISDN,
volume = "29",
number = "8--13",
pages = "1225--1236",
day = "30",
month = sep,
year = "1997",
CODEN = "CNISE9",
ISSN = "0169-7552 (print), 1879-2324 (electronic)",
ISSN-L = "0169-7552",
bibdate = "Fri Sep 24 20:21:54 MDT 1999",
bibsource = "http://www.elsevier.com/cgi-bin/cas/tree/store/cna/cas_free/browse/browse.cgi?year=1997&volume=29&issue=08-13;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.computerworld.com/s/article/9222018/Italian_mathematician_prepares_to_challenge_Google;
http://www.elsevier.com/cgi-bin/cas/tree/store/comnet/cas_sub/browse/browse.cgi?year=1997&volume=29&issue=08-13&aid=1711",
acknowledgement = ack-nhfb,
fjournal = "Computer Networks and ISDN Systems",
journal-URL = "http://www.sciencedirect.com/science/journal/01697552",
remark = "This article is claimed in a 2011-11-21 ComputerWorld
story to be a precursor of the Google PageRank
algorithm, although it refers to it as a 1996
conference article.",
}
@Article{Brin:1998:ALS,
author = "Sergey Brin and Lawrence Page",
title = "The anatomy of a large-scale hypertextual {Web} search
engine",
journal = j-COMP-NET-ISDN,
volume = "30",
number = "1--7",
pages = "107--117",
day = "1",
month = apr,
year = "1998",
CODEN = "CNISE9",
ISSN = "0169-7552 (print), 1879-2324 (electronic)",
ISSN-L = "0169-7552",
bibdate = "Fri Sep 24 20:22:05 MDT 1999",
bibsource = "http://www.elsevier.com/cgi-bin/cas/tree/store/cna/cas_free/browse/browse.cgi?year=1998&volume=30&issue=1-7;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.elsevier.com/cas/tree/store/comnet/sub/1998/30/1-7/1921.pdf",
acknowledgement = ack-nhfb,
fjournal = "Computer Networks and ISDN Systems",
journal-URL = "http://www.sciencedirect.com/science/journal/01697552",
}
@TechReport{Page:1998:PCR,
author = "Lawrence Page and Sergey Brin and Rajeev Motwani and
Terry Winograd",
title = "The {PageRank} Citation Ranking: Bringing Order to the
Web",
institution = "Stanford Digital Library Technologies Project,
Stanford University",
address = "Stanford, CA, USA",
pages = "17",
day = "11",
month = nov,
year = "1998",
bibdate = "Thu Oct 24 15:13:54 2002",
bibsource = "https://www.math.utah.edu/pub/tex/bib/master.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://dbpubs.stanford.edu/pub/1999-66;
http://ilpubs.Stanford.edu:8090/422/",
abstract = "The importance of a Web page is an inherently
subjective matter, which depends on the readers
interests, knowledge and attitudes. But there is still
much that can be said objectively about the relative
importance of Web pages. This paper describes PageRank,
a mathod for rating Web pages objectively and
mechanically, effectively measuring the human interest
and attention devoted to them. We compare PageRank to
an idealized random Web surfer. We show how to
efficiently compute PageRank for large numbers of
pages. And, we show how to apply PageRank to search and
to user navigation.",
acknowledgement = ack-nhfb,
annote = "This is the Google search algorithm.",
}
@TechReport{Page:1999:PCR,
author = "Lawrence Page and Sergey Brin and Rajeev Motwani and
Terry Winograd",
title = "The {PageRank} Citation Ranking: Bringing Order to the
Web",
type = "Technical Report",
number = "1999-66",
institution = "Stanford Digital Library Technologies Project,
Stanford University",
address = "Stanford, CA, USA",
year = "1999",
bibdate = "Tue Aug 11 17:32:19 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Finkelstein:2001:PSC,
author = "Lev Finkelstein and Evgeniy Gabrilovich and Yossi
Matias and Ehud Rivlin and Zach Solan and Gadi Wolfman
and Eytan Ruppin",
title = "Placing search in context: the concept revisited",
crossref = "ACM:2001:CPT",
pages = "406--414",
year = "2001",
DOI = "https://doi.org/10.1145/371920.372094",
bibdate = "Mon May 10 14:07:25 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Kruschwitz:2001:WKD,
author = "U. Kruschwitz",
booktitle = "{IEEE International Conference on Systems, Man, and
Cybernetics, 2001, 7--10 October, 2001, Tucson, AZ}",
title = "World knowledge for the domain of your choice",
crossref = "Bahill:2001:IIC",
pages = "555--560",
year = "2001",
DOI = "https://doi.org/10.1109/ICSMC.2001.969872",
bibdate = "Thu May 06 13:31:24 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Modern Web search engines access large parts of the
publicly indexable Web. Relevant sites can be found
easily thanks to advanced techniques such as Google's
PageRank algorithm. However, a common problem remains
the large number of matching documents being returned
even for fairly specific queries. The same problem can
be observed in domains that are more limited like
intranets or local Web sites. By enriching a search
engine with knowledge about the domain one could
provide much more feedback for a query than just a list
of matches, such as a number of useful discriminating
terms, that would allow the user to constrain the
query. We present a way of building such a domain model
automatically by analyzing the markup of the source
data. We will illustrate this with some examples taken
from our sample domain.",
acknowledgement = ack-nhfb,
}
@InProceedings{Miller:2001:MKH,
author = "Joel C. Miller and Gregory Rae and Fred Schaefer and
Lesley A. Ward and Thomas LoFaro and Ayman Farahat",
editor = "Donald H. Kraft",
booktitle = "{Proceedings of the 24th Annual International ACM
SIGIR Conference on Research and Development in
Information Retrieval, SIGIR 01: New Orleans,
Louisiana, USA, September 9--13, 2001}",
title = "Modifications of {Kleinberg}'s {HITS} algorithm using
matrix exponentiation and web log records",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "444--445",
year = "2001",
DOI = "https://doi.org/10.1145/383952.384086",
ISBN = "1-58113-331-6",
ISBN-13 = "978-1-58113-331-8",
LCCN = "QA76.9.D3 I552 2001; Z699.A1",
bibdate = "Tue Aug 11 17:26:34 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Ng:2001:SAL,
author = "Andrew Y. Ng and Alice X. Zheng and Michael I.
Jordan",
title = "Stable algorithms for link analysis",
crossref = "Croft:2001:PAI",
pages = "258--266",
year = "2001",
DOI = "https://doi.org/10.1145/383952.384003",
bibdate = "Wed Jun 01 18:24:42 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "The Kleinberg HITS and the Google PageRank algorithms
are eigenvector methods for identifying
``authoritative'' or ``influential'' articles, given
hyperlink or citation information. That such algorithms
should give reliable or consistent answers is surely a
desideratum, and in \cite{ijcaiPaper}, we analyzed when
they can be expected to give stable rankings under
small perturbations to the linkage patterns. In this
paper, we extend the analysis and show how it gives
insight into ways of designing stable link analysis
methods. This in turn motivates two new algorithms,
whose performance we study empirically using citation
data and web hyperlink data.",
acknowledgement = ack-nhfb,
}
@Misc{Page:2001:MNR,
author = "Lawrence Page",
title = "Method for node ranking in a linked database",
howpublished = "US Patent 6,285,999",
day = "4",
month = sep,
year = "2001",
bibdate = "Thu Jun 02 08:24:11 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "Filed January 9, 1998. Expires around January 9,
2018.",
URL = "http://patft.uspto.gov/netahtml/PTO/srchnum.htm",
abstract = "A method assigns importance ranks to nodes in a linked
database, such as any database of documents containing
citations, the world wide web or any other hypermedia
database. The rank assigned to a document is calculated
from the ranks of documents citing it. In addition, the
rank of a document is calculated from a constant
representing the probability that a browser through the
database will randomly jump to the document. The method
is particularly useful in enhancing the performance of
search engine results for hypermedia databases, such as
the world wide web, whose documents have a large
variation in quality.",
acknowledgement = ack-nhfb,
remark = "This may be the main patent behind the Google search
engine.",
}
@InProceedings{Arasu:2002:PCS,
author = "Arvind Arasu and Jasmine Novak and Andrew Tomkins and
John Tomlin",
title = "{PageRank} Computation and the Structure of the {Web}:
Experiments and Algorithms",
crossref = "Anonymous:2002:PIW",
pages = "??--??",
year = "2002",
bibdate = "Thu Oct 24 15:18:39 2002",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www2002.org/CDROM/",
URL = "https://www.math.utah.edu/pub/tex/bib/master.bib;
http://www2002.org/CDROM/poster/173.pdf",
acknowledgement = ack-nhfb,
annote = "PageRank is the Google search algorithm.",
pagecount = "5",
}
@InProceedings{Chen:2002:ETC,
author = "Yen-Yu Chen and Qingqing Gan and Torsten Suel",
editor = "{ACM}",
booktitle = "Conference on Information and Knowledge Management
Proceedings of the eleventh international conference on
Information and knowledge management",
title = "{I/O}-efficient techniques for computing {PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "549--557",
year = "2002",
DOI = "https://doi.org/10.1145/238386.238450",
ISBN = "1-58113-492-4",
ISBN-13 = "978-1-58113-492-6",
LCCN = "????",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Over the last few years, most major search engines
have integrated link-based ranking techniques in order
to provide more accurate search results. One widely
known approach is the Pagerank technique, which forms
the basis of the Google ranking scheme, and which
assigns a global importance measure to each page based
on the importance of other pages pointing to it. The
main advantage of the Pagerank measure is that it is
independent of the query posed by a user; this means
that it can be precomputed and then used to optimize
the layout of the inverted index structure accordingly.
However, computing the Pagerank measure requires
implementing an iterative process on a massive graph
corresponding to billions of web pages and
hyperlinks.In this paper, we study I/O-efficient
techniques to perform this iterative computation. We
derive two algorithms for Pagerank based on techniques
proposed for out-of-core graph algorithms, and compare
them to two existing algorithms proposed by Haveliwala.
We also consider the implementation of a recently
proposed topic-sensitive version of Pagerank. Our
experimental results show that for very large data
sets, significant improvements over previous results
can be achieved on machines with moderate amounts of
memory. On the other hand, at most minor improvements
are possible on data sets that are only moderately
larger than memory, which is the case in many practical
scenarios.",
acknowledgement = ack-nhfb,
keywords = "external memory algorithms; link-based ranking;
out-of-core; pagerank; search engines",
}
@InProceedings{Chen:2002:UFW,
author = "Zheng Chen and Li Tao and Jidong Wang and Liu Wenyin
and Wei-Ying Ma",
title = "A unified framework for {Web} link analysis",
crossref = "WangLing:2002:PTI",
pages = "63--70",
year = "2002",
DOI = "https://doi.org/10.1109/WISE.2002.1181644",
bibdate = "Thu May 06 14:00:37 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@TechReport{Colley:2002:CBF,
author = "W. N. Colley",
title = "{Colley}'s Bias Free College Football Ranking Method:
The {Colley} Matrix Explained",
type = "Technical Report",
institution = "Princeton University",
address = "Princeton, NJ, USA",
year = "2002",
bibdate = "Tue Aug 11 16:32:30 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.colleyrankings.com/matrate.pdf",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@Article{Dhyani:2002:SWM,
author = "Devanshu Dhyani and Wee Keong Ng and Sourav S.
Bhowmick",
title = "A survey of {Web} metrics",
journal = j-COMP-SURV,
volume = "34",
number = "4",
pages = "469--503",
month = dec,
year = "2002",
CODEN = "CMSVAN",
DOI = "https://doi.org/10.1145/592642.592645",
ISSN = "0360-0300 (print), 1557-7341 (electronic)",
ISSN-L = "0360-0300",
bibdate = "Thu Jun 19 10:18:33 MDT 2008",
bibsource = "http://www.acm.org/pubs/contents/journals/surveys/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/compsurv.bib",
abstract = "The unabated growth and increasing significance of the
World Wide Web has resulted in a flurry of research
activity to improve its capacity for serving
information more effectively. But at the heart of these
efforts lie implicit assumptions about `quality' and
`usefulness' of Web resources and services. This
observation points towards measurements and models that
quantify various attributes of web sites. The science
of measuring all aspects of information, especially its
storage and retrieval or informetrics has interested
information scientists for decades before the existence
of the Web. Is Web informetrics any different, or is it
just an application of classical informetrics to a new
medium? In this article, we examine this issue by
classifying and discussing a wide ranging set of Web
metrics. We present the origins, measurement functions,
formulations and comparisons of well-known Web metrics
for quantifying Web graph properties, Web page
significance, Web page similarity, search and
retrieval, usage characterization and information
theoretic properties. We also discuss how these metrics
can be applied for improving Web information access and
use.",
acknowledgement = ack-nhfb,
fjournal = "ACM Computing Surveys",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J204",
keywords = "Information theoretic; PageRank; quality metrics; Web
graph; Web metrics; Web page similarity",
}
@InProceedings{Ding:2002:PHU,
author = "Chris Ding and Xiaofeng He and Parry Husbands and
Hongyuan Zha and Horst D. Simon",
editor = "{ACM}",
booktitle = "Annual ACM Conference on Research and Development in
Information Retrieval Proceedings of the 25th annual
international ACM SIGIR conference on Research and
development in information retrieval",
title = "PageRank, {HITS} and a unified framework for link
analysis",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "353--354",
year = "2002",
DOI = "https://doi.org/10.1145/324133.324140",
ISBN = "1-58113-561-0",
ISBN-13 = "978-1-58113-561-9",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Two popular link-based webpage ranking algorithms are
(i) PageRank[1] and (ii) HITS (Hypertext Induced Topic
Selection)[3]. HITS makes the crucial distinction of
hubs and authorities and computes them in a mutually
reinforcing way. PageRank considers the hyperlink
weight normalization and the equilibrium distribution
of random surfers as the citation score. We generalize
and combine these key concepts into a unified
framework, in which we prove that rankings produced by
PageRank and HITS are both highly correlated with the
ranking by in-degree and out-degree.",
acknowledgement = ack-nhfb,
}
@InProceedings{Haveliwala:2002:TSP,
author = "Taher H. Haveliwala",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceedings of
the 11th international conference on World Wide Web",
title = "Topic-sensitive {PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "517--526",
year = "2002",
DOI = "https://doi.org/10.1145/511446.511513",
ISBN = "1-58113-449-5",
ISBN-13 = "978-1-58113-449-0",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In the original PageRank algorithm for improving the
ranking of search-query results, a single PageRank
vector is computed, using the link structure of the
Web, to capture the relative 'importance' of Web pages,
independent of any particular search query. To yield
more accurate search results, we propose computing a
set of PageRank vectors, biased using a set of
representative topics, to capture more accurately the
notion of importance with respect to a particular
topic. By using these (precomputed) biased PageRank
vectors to generate query-specific importance scores
for pages at query time, we show that we can generate
more accurate rankings than with a single, generic
PageRank vector. For ordinary keyword search queries,
we compute the topic-sensitive PageRank scores for
pages satisfying the query using the topic of the query
keywords. For searches done in context (e.g., when the
search query is performed by highlighting words in a
Web page), we compute the topic-sensitive PageRank
scores using the topic of the context in which the
query appeared.",
acknowledgement = ack-nhfb,
keywords = "link structure; PageRank; personalized search; search;
search in context; web graph",
}
@InProceedings{Jeh:2002:SMS,
author = "Glen Jeh and Jennifer Widom",
booktitle = "{Proceedings of the Eighth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining,
KDD'02: July 23--36, 2002, Edmonton, Alberta, Canada}",
title = "{SimRank}: A measure of structural-context
similarity",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "538--543",
year = "2002",
DOI = "https://doi.org/10.1145/775047.775126",
ISBN = "1-58113-567-X",
ISBN-13 = "978-1-58113-567-1",
LCCN = "QA76.9.D3 I58 2002",
bibdate = "Tue Aug 11 17:08:35 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
bookpages = "xiv + 704",
}
@Article{Kim:2002:ICP,
author = "Sung Jin Kim and Sang Ho Lee",
title = "An Improved Computation of the {PageRank} Algorithm",
journal = j-LECT-NOTES-COMP-SCI,
volume = "2291",
pages = "73--85",
year = "2002",
CODEN = "LNCSD9",
ISBN = "3-540-43343-0",
ISBN-13 = "978-3-540-43343-9",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "http://link.springer-ny.com/link/service/series/0558/tocs/t2291.htm;
https://www.math.utah.edu/pub/tex/bib/lncs2002a.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2291/22910073.htm;
http://link.springer-ny.com/link/service/series/0558/papers/2291/22910073.pdf",
ZMnumber = "1056.68526",
acknowledgement = ack-nhfb,
fjournal = "Lecture Notes in Computer Science",
}
@TechReport{Moler:2002:CCW,
author = "Cleve B. Moler",
title = "{Cleve}'s Corner: The World's Largest Matrix
Computation: {Google}'s {PageRank} is an eigenvector of
a matrix of order $ 2.7 $ billion",
type = "Technical note",
institution = inst-MATHWORKS,
address = inst-MATHWORKS:adr,
pages = "1",
month = oct,
year = "2002",
bibdate = "Thu Oct 24 07:16:21 2002",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/m/moler-cleve-b.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.mathworks.com/company/newsletter/clevescorner/oct02_cleve.shtml",
acknowledgement = ack-nhfb,
keywords = "Matlab",
}
@Article{Pandurangan:2002:UPC,
author = "Gopal Pandurangan and Prabhakar Raghavan and Eli
Upfal",
title = "Using {PageRank} to characterize {Web} structure",
journal = j-LECT-NOTES-COMP-SCI,
volume = "2387",
pages = "330--339",
year = "2002",
CODEN = "LNCSD9",
DOI = "https://doi.org/10.1007/3-540-45655-4_36",
ISBN = "3-540-43996-X",
ISBN-13 = "978-3-540-43996-7",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
LCCN = "????",
MRclass = "68M10 68U35",
MRnumber = "MR2064528",
bibdate = "Tue Sep 10 19:10:08 MDT 2002",
bibsource = "http://link.springer-ny.com/link/service/series/0558/tocs/t2387.htm;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2387/23870330.htm;
http://link.springer-ny.com/link/service/series/0558/papers/2387/23870330.pdf",
ZMnumber = "1077.68527",
acknowledgement = ack-nhfb,
fjournal = "Lecture Notes in Computer Science",
}
@Article{Pretto:2002:TAG,
author = "Luca Pretto",
title = "A Theoretical Analysis of {Google}'s {PageRank}",
journal = j-LECT-NOTES-COMP-SCI,
volume = "2476",
pages = "131--144",
year = "2002",
CODEN = "LNCSD9",
ISBN = "3-540-44158-1",
ISBN-13 = "978-3-540-44158-8",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
LCCN = "????",
bibdate = "Sat Nov 30 20:57:37 MST 2002",
bibsource = "http://link.springer-ny.com/link/service/series/0558/tocs/t2476.htm;
https://www.math.utah.edu/pub/tex/bib/lncs2002e.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.de/link/service/series/0558/bibs/2476/24760131.htm;
http://link.springer.de/link/service/series/0558/papers/2476/24760131.pdf",
acknowledgement = ack-nhfb,
fjournal = "Lecture Notes in Computer Science",
}
@Book{Baldi:2003:MIW,
author = "Pierre Baldi and Paolo Frasconi and Padhraic Smyth",
title = "Modeling the {Internet} and the {Web}: probabilistic
methods and algorithms",
publisher = pub-WILEY,
address = pub-WILEY:adr,
pages = "xix + 285",
year = "2003",
ISBN = "0-470-86492-3 (e-book), 0-470-84906-1",
ISBN-13 = "978-0-470-86492-0 (e-book), 978-0-470-84906-4",
LCCN = "TK5105.875.I57 B35 2003eb",
bibdate = "Fri Jun 3 10:03:23 MDT 2011",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
subject = "Internet; Mathematical models; Telecommunication;
Traffic; World Wide Web; Cyberspace; Probabilities;
Computers; Web; General; Networking; Intranets and
Extranets",
tableofcontents = "Mathematical Background \\
Probability and Learning from a Bayesian Perspective
\\
Parameter Estimation from Data \\
Basic principles \\
A simple die example \\
Mixture Models and the Expectation Maximization
Algorithm \\
Graphical Models \\
Bayesian networks \\
Belief propagation \\
Learning directed graphical models from data \\
Classification \\
Clustering \\
Power-Law Distributions \\
Scale-free properties (80/20 rule) \\
Applications to Languages: Zipf's and Heaps' Laws \\
Origin of power-law distributions and Fermi's model \\
Basic WWW Technologies \\
Web Documents \\
SGML and HTML \\
General structure of an HTML document \\
Links \\
Resource Identifiers: URI, URL, and URN \\
Protocols \\
Reference models and TCP/IP \\
The domain name system \\
The Hypertext Transfer Protocol \\
Programming examples \\
Log Files \\
Search Engines \\
Coverage \\
Basic crawling \\
Web Graphs \\
Internet and Web Graphs \\
Power-law size \\
Power-law connectivity \\
Small-world networks \\
Power law of PageRank \\
The bow-tie structure \\
Generative Models for the Web Graph and Other Networks
\\
Web page growth \\
Lattice perturbation models: between order and disorder
\\
Preferential attachment models, or the rich get richer
\\
Copy models \\
PageRank models \\
Applications \\
Distributed search algorithms \\
Subgraph patterns and communities \\
Robustness and vulnerability \\
Notes and Additional Technical References \\
Text Analysis \\
Indexing \\
Compression techniques \\
Lexical Processing \\
Tokenization",
}
@InProceedings{Bianchini:2003:PWC,
author = "M. Bianchini and M. Gori and F. Scarselli",
title = "{PageRank} and {Web} communities",
crossref = "Liu:2003:ISW",
pages = "365--371",
year = "2003",
DOI = "https://doi.org/10.1109/WI.2003.1241217",
bibdate = "Fri Feb 19 18:30:00 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1241217",
abstract = "The definition of the ordering of the Web pages,
returned on a given query, is a crucial topic, which
gives rise to the notion of Web visibility. A
fundamental contribution towards the conception of
appropriate ordering criteria has been given by means
of the introduction of PageRank, which takes into
account only the hyper-linked structure of the Web,
regardless of the content of the pages. In this paper,
we introduce a circuit analysis which allows us to
understand the distribution of PageRank, and show some
basic results for understanding the way it migrates
amongst communities. In particular, we highlight some
topological properties which suggest methods for the
promotion of Web communities. These results confirm the
importance and the effectiveness of PageRank for
discovering relevant information but, at the same time,
point out its vulnerability to spamming.",
acknowledgement = ack-nhfb,
}
@InProceedings{Chirita:2003:FRH,
author = "P.-A. Chirita and D. Olmedilla and W. Nejdl",
booktitle = "{First Latin American Web Congress, 2003. LA-WEB 2003,
Santiago, Chile, November 10--12, 2003. Proceedings}",
title = "Finding related hubs and authorities",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "214--215",
year = "2003",
ISBN = "0-7695-2058-8",
ISBN-13 = "978-0-7695-2058-2",
LCCN = "????",
bibdate = "Mon May 10 12:22:33 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
bookpages = "xii + 241",
keywords = "PageRank",
}
@InProceedings{Ding:2003:PHU,
author = "C. H. Q. Ding and X. He and P. Husbands and H. Zha and
H. D. Simon",
title = "{PageRank}: {HITS} and a unified framework for link
analysis",
crossref = "Barbara:2003:PTS",
pages = "353--354",
year = "2003",
bibdate = "Fri Feb 19 15:15:08 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@TechReport{Haveliwala:2003:ACA,
author = "Tamer Haveliwala and Sepandar Kamvar and Glen Jeh",
title = "An analytical comparison of approaches to
personalizing {PageRank}",
type = "Technical report",
number = "2003-32",
institution = "Stanford InfoLab, Stanford University",
address = "Stanford, CA, USA",
pages = "4",
month = jun,
year = "2003",
bibdate = "Tue Jul 20 16:03:24 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ilpubs.stanford.edu:8090/596/",
abstract = "PageRank, the popular link-analysis algorithm for
ranking web pages, assigns a query and user independent
estimate of ``importance'' to web pages. Query and user
sensitive extensions of PageRank, which use a basis set
of biased PageRank vectors, have been proposed in order
to personalize the ranking function in a tractable way.
We analytically compare three recent approaches to
personalizing PageRank and discuss the tradeoffs of
each one.",
acknowledgement = ack-nhfb,
}
@Article{Haveliwala:2003:TSP,
author = "Taher H. Haveliwala",
title = "Topic-sensitive {PageRank}: a context-sensitive
ranking algorithm for {Web} search",
journal = j-IEEE-TRANS-KNOWL-DATA-ENG,
volume = "15",
number = "4",
pages = "784--796",
month = jul,
year = "2003",
CODEN = "ITKEEH",
DOI = "https://doi.org/10.1109/TKDE.2003.1208999",
ISSN = "1041-4347",
ISSN-L = "1041-4347",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1208999",
abstract = "The original PageRank algorithm for improving the
ranking of search-query results computes a single
vector, using the link structure of the Web, to capture
the relative ``importance'' of Web pages, independent
of any particular search query. To yield more accurate
search results, we propose computing a set of PageRank
vectors, biased using a set of representative topics,
to capture more accurately the notion of importance
with respect to a particular topic. For ordinary
keyword search queries, we compute the topic-sensitive
PageRank scores for pages satisfying the query using
the topic of the query keywords. For searches done in
context (e.g., when the search query is performed by
highlighting words in a Web page), we compute the
topic-sensitive PageRank scores using the topic of the
context in which the query appeared. By using linear
combinations of these (precomputed) biased PageRank
vectors to generate context-specific importance scores
for pages at query time, we show that we can generate
more accurate rankings than with a single, generic
PageRank vector. We describe techniques for efficiently
implementing a large-scale search system based on the
topic-sensitive PageRank scheme.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=27216",
fjournal = "IEEE Transactions on Knowledge and Data Engineering",
journal-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69",
keywords = "link analysis; PageRank; personalized search; ranking
algorithm.; search in context; web graph; Web search",
}
@InProceedings{Jeh:2003:SPW,
author = "Glen Jeh and Jennifer Widom",
title = "Scaling personalized web search",
crossref = "Hencsey:2003:PTI",
year = "2003",
DOI = "https://doi.org/10.1145/775152.775191",
bibdate = "Mon May 10 14:17:38 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Recent web search techniques augment traditional text
matching with a global notion of ``importance'' based
on the linkage structure of the web, such as in
Google's PageRank algorithm. For more refined searches,
this global notion of importance can be specialized to
create personalized views of importance--for example,
importance scores can be biased according to a
user-specified set of initially-interesting pages.
Computing and storing all possible personalized views
in advance is impractical, as is computing personalized
views at query time, since the computation of each view
requires an iterative computation over the web graph.
We present new graph-theoretical results, and a new
technique based on these results, that encode
personalized views as partial vectors. Partial vectors
are shared across multiple personalized views, and
their computation and storage costs scale well with the
number of views. Our approach enables incremental
computation, so that the construction of personalized
views from partial vectors is practical at query time.
We present efficient dynamic programming algorithms for
computing partial vectors, an algorithm for
constructing personalized views from partial vectors,
and experimental results demonstrating the
effectiveness and scalability of our techniques.",
acknowledgement = ack-nhfb,
}
@TechReport{Kamvar:2003:EBS,
author = "Sepandar D. Kamvar and Taher H. Haveliwala and
Christopher D. Manning and Gene H. Golub",
title = "Exploiting the block structure of the {Web} for
computing {PageRank}",
type = "Technical Report",
number = "2003-17",
institution = "Stanford InfoLab, Stanford University",
address = "Stanford, CA, USA",
pages = "????",
year = "2003",
bibdate = "Fri Feb 19 15:17:26 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "BlockRank; PageRank",
}
@InProceedings{Kamvar:2003:EMA,
author = "Sepandar D. Kamvar and Taher H. Haveliwala and
Christopher D. Manning and Gene H. Golub",
title = "Extrapolation Methods for Accelerating {PageRank}
Computations",
crossref = "Hencsey:2003:PTI",
pages = "261--270",
year = "2003",
DOI = "https://doi.org/10.1145/775152.775190",
bibdate = "Wed Nov 10 16:22:54 2004",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://dbpubs.stanford.edu:8090/pub/2003-16;
http://www.stanford.edu/~sdkamvar/papers/extrapolation.pdf",
abstract = "We present a novel algorithm for the fast computation
of PageRank, a hyperlink-based estimate of the
``importance'' of Web pages. The original PageRank
algorithm uses the Power Method to compute successive
iterates that converge to the principal eigenvector of
the Markov matrix representing the Web link graph. The
algorithm presented here, called Quadratic
Extrapolation, accelerates the convergence of the Power
Method by periodically subtracting off estimates of the
nonprincipal eigenvectors from the current iterate of
the Power Method. In Quadratic Extrapolation, we take
advantage of the fact that the first eigenvalue of a
Markov matrix is known to be 1 to compute the
nonprincipal eigenvectors using successive iterates of
the Power Method. Empirically, we show that using
Quadratic Extrapolation speeds up PageRank computation
by 25--300\% on a Web graph of 80 million nodes, with
minimal overhead. Our contribution is useful to the
PageRank community and the numerical linear algebra
community in general, as it is a fast method for
determining the dominant eigenvector of a matrix that
is too large for standard fast methods to be
practical.",
acknowledgement = ack-nhfb,
keywords = "eigenvector computation; link analysis; PageRank",
}
@Article{Kang:2003:IPN,
author = "In-Ho Kang and Eun-Jung Oh and Gil Chang Kim",
title = "Incremental {PageRanking} for Newly Crawled {Web}
Pages",
journal = j-INT-J-COMP-PROC-ORIENTAL-LANG,
volume = "16",
number = "1",
pages = "87--??",
month = mar,
year = "2003",
CODEN = "????",
ISSN = "0219-4279",
bibdate = "Thu Jan 06 07:59:01 2005",
bibsource = "http://ejournals.wspc.com.sg/ijcpol/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/ijcpol.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Computer Processing of
Oriental Languages (IJCPOL)",
}
@InProceedings{Narayan:2003:TCW,
author = "B. L. Narayan and C. A. Murthy and S. K. Pal",
title = "Topic continuity for {Web} document categorization and
ranking",
crossref = "Liu:2003:ISW",
pages = "310--315",
year = "2003",
bibdate = "Thu May 06 13:46:52 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Ohmukai:2003:PPC,
author = "I. Ohmukai and H. Takeda and M. Miki",
title = "A proposal of the person-centered approach for
personal task management",
crossref = "Helal:2003:SAI",
pages = "234--240",
year = "2003",
bibdate = "Thu May 06 13:51:30 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "This paper proposes a human-centered approach for
personal task management in which people can decide
management of their tasks according to their
environments, including their subjective and
multivalent judgement and human relationships. In our
approach task management is modeled as a
decision-making process on their own resources. The
human decision-making process consists of three types
of activity, i.e., the intelligence activity, design
activity, and choice activity. The proposed system
assists each activity by three sub-systems, i.e.,
visualizer, optimizer and recommender respectively. At
first, visualizer indicates the attributes associated
with each task such as deadline, subjective priority,
and workload, which are determined by the user. The
optimizer generates executable schedules from these
tasks using an active scheduler and multi-objective
genetic algorithm. Finally, the recommender evaluates
these alternatives using an analytic hierarchy process.
The system is also able to analyze the human
relationships of the user group using the PageRank
algorithm, and this result is utilized to improve the
performance of the task scheduler. We implement a
client/server system which uses mobile phones and
verify the function of the proposed system along the
lines of two scenarios.",
acknowledgement = ack-nhfb,
}
@InProceedings{Sankaralingam:2003:DPP,
author = "Karthikeyan Sankaralingam and Simha Sethumadhavan and
James C. Browne",
title = "Distributed {PageRank} for {P2P} systems",
crossref = "IEEE:2003:IIS",
pages = "58--68",
year = "2003",
DOI = "https://doi.org/10.1109/HPDC.2003.1210016",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1210016",
abstract = "This paper defines and describes a fully distributed
implementation of Google's highly effective PageRank
algorithm, for 'peer to peer' (P2P) systems. The
implementation is based on chaotic (asynchronous)
iterative solution of linear systems. The P2P
implementation also enables incremental computation of
pageranks as new documents are entered into or deleted
from the network. Incremental update enables
continuously accurate pageranks whereas the currently
centralized web crawl and computation over Internet
documents requires several days. This suggests possible
applicability of the distributed algorithm to pagerank
computations as a replacement for the centralized web
crawler based implementation for Internet documents. A
complete solution of the distributed pagerank
computation for an inplace network converges rapidly
(1\% accuracy in 10 iterations) for large systems
although the time for an iteration may be long. The
incremental computation resulting from addition of a
single document converges extremely rapidly, typically
requiring update path lengths of under 15 nodes even
for large networks and very accurate solutions. This
implementation of PageRank provides a uniform ranking
scheme for documents in P2P systems, and its
integration with P2P keyword search provides one
solution to the network traffic problems engendered by
return of document hits. In basic P2P keyword search,
all the document hits must be returned to the querying
node causing large network traffic. An incremental
keyword search algorithm for P2P keyword search where
document hits are sorted by pagerank, and incrementally
returned to the querying node is proposed and
evaluated. Integration of this algorithm into P2P
keyword search can produce dramatic benefit both in
terms of effectiveness for users and decrease in
network traffic. The incremental search algorithm
provided approximately a ten-fold reduction in network
traffic for two-word and three-word queries.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8591",
}
@Article{Sankaralingam:2003:PCK,
author = "Karthikeyan Sankaralingam and Madhulika Yalamanchi and
Simha Sethumadhavan and James C. Browne",
title = "{Pagerank} Computation and Keyword Search on
Distributed Systems and {P2P} Networks",
journal = j-J-GRID-COMP,
volume = "1",
number = "3",
pages = "291--307",
month = "????",
year = "2003",
CODEN = "????",
ISSN = "1570-7873 (print), 1572-9184 (electronic)",
ISSN-L = "1570-7873",
bibdate = "Sat Dec 4 11:39:32 MST 2004",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.wkap.nl/jrnltoc.htm/1570-7873",
URL = "http://ipsapp008.kluweronline.com/IPS/content/ext/x/J/6160/I/9/A/6/abstract.htm;
https://www.math.utah.edu/pub/tex/bib/jgridcomp.bib",
abstract = "This paper presents a fully distributed computation
for Google's pagerank algorithm. The computation is
based on solution of the matrix equation defining
pageranks by a distributed implementation of
asynchronous iteration. Pageranks for the documents
stored on a web server or on a host in a peer-to-peer
network are computed in place and stored with the
documents. The matrix is never assembled and no crawls
of the web are required. Continuously accurate
pageranks are enabled by incremental computation of
pageranks for documents as they are inserted onto a
network storage host and incremental recomputation of
pageranks when documents are deleted. Intrahost and
intradomain dominance of document link structure is
naturally exploited by the distributed asynchronous
iteration algorithm.\par
Three implementations: (i) a simulation which was
previously reported, (ii) an implementation of the
algorithm in a peer-to-peer computational system and
(iii) an embedding of the computation in web servers,
are described. Application of the three implementations
to three different workloads, two constructed following
power law network models for link distributions and one
derived from the Government document database are
reported. Convergence for computation of a complete set
of pageranks is rapid: 1\% accuracy in 10 or fewer
messages per document. Incremental computation of
pageranks resulting from addition or deletion of
documents also converges rapidly, usually requiring 10
or fewer messages per document. Coupling locally stored
pageranks with the documents in a peer-to-peer network
dramatically diminishes the volume of data which must
be transmitted to satisfy keyword searches in
peer-to-peer networks.\par
The web server implementation shows that the
distributed algorithm can be used to enable web servers
to compute pageranks for the documents they store and
thus potentially enable effective keyword searches for
the documents stored on the web servers of intranets by
utilizing unused processing power of the web servers.",
acknowledgement = ack-nhfb,
fjournal = "Journal of Grid Computing",
journal-URL = "http://link.springer.com/journal/10723",
}
@InProceedings{Shi:2003:DPR,
author = "ShuMing Shi and Jin Yu and GuangWen Yang and DingXing
Wang",
title = "Distributed page ranking in structured {P2P}
networks",
crossref = "Yang:2003:ICP",
pages = "179--186",
year = "2003",
DOI = "https://doi.org/10.1109/ICPP.2003.1240579",
bibdate = "Thu May 06 13:52:53 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We discuss the techniques of performing distributed
page ranking on top of structured peer-to-peer
networks. Distributed page ranking are needed because
the size of the Web grows at a remarkable speed and
centralized page ranking is not scalable. Open system
PageRank is presented based on the traditional PageRank
used by Google. We then propose some distributed page
ranking algorithms, partially prove their convergence,
and discuss some interesting properties of them.
Indirect transmission is introduced to reduce
communication overhead between page rankers and to
achieve scalable communication. The relationship
between convergence time and bandwidth consumed is also
discussed. Finally, we verify some of the discussions
by experiments based on real datasets.",
acknowledgement = ack-nhfb,
}
@Misc{Sobek:2003:PGP,
author = "M. Sobek",
title = "{PR0} --- {Google}'s {PageRank} $0$ Penalty",
howpublished = "Web document.",
year = "2003",
bibdate = "Tue Aug 11 17:36:01 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://pr.efactory.de/e-pr0.shtml",
abstract = "By the end of 2001, the Google search engine
introduced a new kind of penalty for websites that use
questionable search engine optimization tactics: A
PageRank of 0. In search engine optimization forums it
is called PR0 and this term shall also be used here.
Characteristically for PR0 is that all or at least a
lot of pages of a website show a PageRank of 0 in the
Google Toolbar, even if they do have high quality
inbound links. Those pages are not completely removed
from the index but they are always at the end of search
results and, thus, they are hardly to be found.",
acknowledgement = ack-nhfb,
}
@InProceedings{Tao:2003:QSS,
author = "Wen-Xue Tao and Wan-Li Zuo",
booktitle = "{International Conference on Machine Learning and
Cybernetics, 2003}",
title = "Query-sensitive self-adaptable {Web} page ranking
algorithm",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "413--418",
year = "2003",
ISBN = "0-7803-8131-9",
ISBN-13 = "978-0-7803-8131-5",
LCCN = "????",
bibdate = "Thu May 06 13:40:45 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "This paper analyzes HITS and PageRank, two
representative examples of current Web page ranking
algorithms, and points out their limitations in
capturing both global and local importance scopes. A
detailed discussion is also conducted regarding the
reasons why manually setting topics adopted by
topic-sensitive PageRank algorithm cannot resolve the
same problem. Based on the above observation, a new
query-sensitive algorithm termed QS page-rank
satisfying both global and local authority is
introduced, and several strategies for combining our
algorithm with traditional PageRank are also proposed.
Experiment results show effectiveness of the new page
ranking algorithm.",
acknowledgement = ack-nhfb,
}
@InProceedings{Tomlin:2003:NPR,
author = "John A. Tomlin",
editor = "Bebo White and Gusztav Hencsey",
booktitle = "{Proceedings of the 12th International Conference on
the World Wide Web, WWW '03}",
title = "A new paradigm for ranking pages on the {World Wide
Web, Budapest, Hungary, May 20--24, 2003}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "350--355",
year = "2003",
DOI = "https://doi.org/10.1145/775152.775202",
ISBN = "1-58113-680-3",
ISBN-13 = "978-1-58113-680-7",
LCCN = "TK5105.888 I573 2003",
bibdate = "Tue Aug 11 17:37:30 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
bookpages = "xx + 752",
}
@InProceedings{Acharyya:2004:OEP,
author = "Sreangsu Acharyya and Joydeep Ghosh",
editor = "{ACM}",
booktitle = "{International World Wide Web Conference Proceedings
of the 13th international World Wide Web conference:
Alternate track papers \& posters}",
title = "Outlink estimation for {PageRank} computation under
missing data",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "486--487",
year = "2004",
ISBN = "1-58113-912-8",
ISBN-13 = "978-1-58113-912-9",
LCCN = "????",
bibdate = "Sat May 8 18:33:05 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "The enormity and rapid growth of the web-graph forces
quantities such as its pagerank to be computed under
missing information consisting of outlinks of pages
that have not yet been crawled. This paper examines the
role played by the size and distribution of this
missing data in determining the accuracy of the
computed pagerank, focusing on questions such as (i)
the accuracy of pageranks under missing information,
(ii) the size at which a crawl process may be aborted
while still ensuring reasonable accuracy of pageranks,
and (iii) algorithms to estimate pageranks under such
missing information. The first couple of questions are
addressed on the basis of certain simple bounds
relating the expected distance between the true and
computed pageranks and the size of the missing data.
The third question is explored by devising algorithms
to predict the pageranks when full information is not
available. A key feature of the 'dangling link
estimation' and 'clustered link estimation' algorithms
proposed is that, they do not need to run the pagerank
iteration afresh once the outlinks have been
estimated.",
acknowledgement = ack-nhfb,
}
@InProceedings{Altman:2004:RSP,
author = "Alon Altman",
booktitle = "????",
title = "Ranking systems: the {PageRank} axioms",
volume = "05011",
publisher = "Internat. Begegnungs- und Forschungszentrum f{\"u}r
Informatik",
year = "2004",
bibdate = "Fri Feb 19 15:35:56 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Dagstuhl seminar proceedings",
acknowledgement = ack-nhfb,
}
@Misc{Anonymous:2004:BGB,
author = "Anonymous",
title = "Biography: The {Google} boys",
howpublished = "A\&E Television Networks",
address = "United States",
day = "18",
month = dec,
year = "2004",
bibdate = "Fri Jun 3 09:47:20 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
note = "1 50-minute VHS videocassette.",
abstract = "Profiles Sergey Brin and Larry Page, two Stanford
University computer science Ph.D candidates who went on
to develop the world's most popular search engine.",
acknowledgement = ack-nhfb,
subject = "Brin, Sergey; Page, Larry; Internet industry; United
States; History; Businesspeople; Biography",
subject-dates = "1973--; 1973--",
}
@InProceedings{Balmin:2004:OAB,
author = "A. Balmin and V. Hristidis and Y. Papakonstantinou",
editor = "Mario A. Nascimento and M. Tamer {\"O}zsu and Donald
Kossmann and Ren{\'e}e J. Miller and Jos{\'e} A.
Blakeley and K. Bernhard Schiefer",
booktitle = "Proceedings of the Thirtieth International Conference
on Very Large Data Bases: VLDB '04. Toronto, Canada,
Aug. 31--Sept. 3, 2004",
title = "{ObjectRank}: Authority-based keyword search in
databases",
publisher = pub-MORGAN-KAUFMANN,
address = pub-MORGAN-KAUFMANN:adr,
pages = "564--575",
year = "2004",
ISBN = "0-12-088469-0 (paperback)",
ISBN-13 = "978-0-12-088469-8 (paperback)",
LCCN = "QA76.9.D3 I559 2004",
bibdate = "Tue Aug 11 15:55:54 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
bookpages = "1380",
}
@Article{Blondel:2004:MSB,
author = "Vincent D. Blondel and Anah{\'\i} Gajardo and Maureen
Heymans and Pierre Senellart and Paul {Van Dooren}",
title = "A Measure of Similarity between Graph Vertices:
Applications to Synonym Extraction and {Web}
Searching",
journal = j-SIAM-REVIEW,
volume = "46",
number = "4",
pages = "647--666",
month = dec,
year = "2004",
CODEN = "SIREAD",
DOI = "https://doi.org/10.1137/S0036144502415960",
ISSN = "0036-1445 (print), 1095-7200 (electronic)",
ISSN-L = "0036-1445",
bibdate = "Sat Mar 29 09:56:54 MDT 2014",
bibsource = "http://epubs.siam.org/toc/siread/46/4;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/siamreview.bib",
URL = "http://epubs.siam.org/sam-bin/dbq/article/41596",
acknowledgement = ack-nhfb,
fjournal = "SIAM Review",
journal-URL = "http://epubs.siam.org/sirev",
onlinedate = "January 2004",
}
@InProceedings{Boldi:2004:DYW,
author = "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
title = "Do your worst to make the best: Paradoxical effects in
{PageRank} incremental computations",
crossref = "Leonardi:2004:AMW",
pages = "168--180",
year = "2004",
MRclass = "68M10",
bibdate = "Thu May 06 12:24:30 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1109.68325",
acknowledgement = ack-nhfb,
}
@InProceedings{Broder:2004:EPA,
author = "Andrei Z. Broder and Ronny Lempel and Farzin Maghoul
and Jan Pedersen",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceedings of
the 13th international World Wide Web conference:
Alternate track papers \& posters",
title = "Efficient {PageRank} approximation via graph
aggregation",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "484--485",
year = "2004",
DOI = "https://doi.org/10.1145/1013367.1013537",
ISBN = "1-58113-912-8",
ISBN-13 = "978-1-58113-912-9",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We present a framework for approximating random-walk
based probability distributions over Web pages using
graph aggregation. We (1) partition the Web's graph
into classes of quasi-equivalent vertices, (2) project
the page-based random walk to be approximated onto
those classes, and (3) compute the stationary
probability distribution of the resulting class-based
random walk. From this distribution we can quickly
reconstruct a distribution on pages. In particular, our
framework can approximate the well-known PageRank
distribution by setting the classes according to the
set of pages on each Web host. We experimented on a
Web-graph containing over 1.4 billion pages, and were
able to produce a ranking that has Spearman rank-order
correlation of 0.95 with respect to PageRank. A
simplistic implementation of our method required less
than half the running time of a highly optimized
implementation of PageRank, implying that larger
speedup factors are probably possible.",
acknowledgement = ack-nhfb,
keywords = "link analysis; search engines; web information
retrieval",
}
@InProceedings{Chen:2004:LME,
author = "Yen-Yu Chen and Qingqing Gan and Torsten Suel",
editor = "{ACM}",
booktitle = "Conference on Information and Knowledge Management
Proceedings of the thirteenth ACM international
conference on Information and knowledge management",
title = "Local methods for estimating {PageRank} values",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "381--389",
year = "2004",
DOI = "https://doi.org/10.1145/383952.384003",
ISBN = "1-58113-874-1",
ISBN-13 = "978-1-58113-874-0",
LCCN = "????",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "The Google search engine uses a method called
PageRank, together with term-based and other ranking
techniques, to order search results returned to the
user. PageRank uses link analysis to assign a global
importance score to each web page. The PageRank scores
of all the pages are usually determined off-line in a
large-scale computation on the entire hyperlink graph
of the web, and several recent studies have focused on
improving the efficiency of this computation, which may
require multiple hours on a workstation. \par
However, in some scenarios, such as online analysis of
link evolution and mining of large web archives such as
the Internet Archive, it may be desirable to quickly
approximate or update the PageRanks of individual nodes
without performing a large-scale computation on the
entire graph. We address this problem by studying
several methods for efficiently estimating the PageRank
score of a particular web page using only a small
subgraph of the entire web. In our model, we assume
that the graph is accessible remotely via a link
database (such as the AltaVista Connectivity Server) or
is stored in a relational database that performs
lookups on disks to retrieve node and connectivity
information. We show that a reasonable estimate of the
PageRank value of a node is possible in most cases by
retrieving only a moderate number of nodes in the local
neighborhood of the node.",
acknowledgement = ack-nhfb,
keywords = "external memory algorithms; link database; link-based
ranking; out-of-core; pagerank; search engines",
}
@InProceedings{Chirita:2004:FRP,
author = "P. Chirita and D. Olmedilla and W. Nejdl",
title = "Finding Related Pages Using the Link Structure of the
{WWW}",
crossref = "Zhong:2004:IWS",
pages = "632--635",
year = "2004",
DOI = "https://doi.org/10.1109/WI.2004.10056",
bibdate = "Thu May 06 14:07:03 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Article{Ding:2004:LAH,
author = "Chris H. Q. Ding and Hongyuan Zha and Xiaofeng He and
Parry Husbands and Horst D. Simon",
title = "Link Analysis: Hubs and Authorities on the {World Wide
Web}",
journal = j-SIAM-REVIEW,
volume = "46",
number = "2",
pages = "256--268",
month = jun,
year = "2004",
CODEN = "SIREAD",
DOI = "https://doi.org/10.1137/S0036144501389218",
ISSN = "0036-1445 (print), 1095-7200 (electronic)",
ISSN-L = "0036-1445",
bibdate = "Sat Apr 16 12:47:29 MDT 2005",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SIREV/46/2;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://epubs.siam.org/sam-bin/dbq/article/38921",
acknowledgement = ack-nhfb,
fjournal = "SIAM Review",
journal-URL = "http://epubs.siam.org/sirev",
}
@TechReport{Gleich:2004:FPP,
author = "D. Gleich and L. Zhukov and P. Berkhin",
title = "Fast Parallel {PageRank}: a Linear System Approach",
type = "Technical Report",
number = "YRL-2004-038",
institution = "Yahoo! Research",
address = "????",
year = "2004",
bibdate = "Wed Nov 30 08:08:31 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http:research.yahoo.com/publication/YRL-2004-035.pdf",
acknowledgement = ack-nhfb,
}
@InProceedings{Gyongyi:2004:CWS,
author = "Z. Gy{\"o}ngyi and H. Garcia-Molina and J. Pedersen",
editor = "Mario A. Nascimento and M. Tamer {\"O}zsu and Donald
Kossmann and Ren{\'e}e J. Miller and Jos{\'e} A.
Blakeley and K. Bernhard Schiefer",
booktitle = "Proceedings of the Thirtieth International Conference
on Very Large Data Bases: VLDB '04. Toronto, Canada,
Aug. 31--Sept. 3, 2004",
title = "Combating web spam with {TrustRank}",
publisher = pub-MORGAN-KAUFMANN,
address = pub-MORGAN-KAUFMANN:adr,
pages = "576--587",
year = "2004",
DOI = "https://doi.org/10.1016/B978-012088469-8.50052-8",
ISBN = "0-12-088469-0 (paperback), 0-12-722442-4,
0-08-053979-3 (e-book)",
ISBN-13 = "978-0-12-088469-8 (paperback), 978-0-12-722442-8,
978-0-08-053979-9 (e-book)",
LCCN = "QA76.9.D3 I559 2004",
bibdate = "Tue Aug 11 17:00:09 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/B9780120884698500528",
acknowledgement = ack-nhfb,
book-URL = "http://www.sciencedirect.com/science/book/9780120884698",
bookpages = "1380",
}
@InProceedings{Ingongngam:2004:TCA,
author = "P. Ingongngam and A. Rungsawang",
title = "Topic-centric algorithm: a novel approach to {Web}
link analysis",
crossref = "Barolli:2004:ICA",
pages = "299--301",
year = "2004",
DOI = "https://doi.org/10.1109/AINA.2004.1283807",
bibdate = "Thu May 06 14:11:27 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Misc{Kamvar:2004:ACR,
author = "Sepandar D. Kamvar and Taher H. Haveliwala and Gene H.
Golub",
title = "Adaptive computation of ranking",
howpublished = "US Patent 7,028,029.",
day = "23",
month = aug,
year = "2004",
bibdate = "Wed Jun 01 18:43:31 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://patft.uspto.gov/netahtml/PTO/srchnum.htm",
abstract = "A system and method is disclosed in which a ranking
function for a set of document rank values is
iteratively solved with respect to a set of linked
documents until a first stability condition is
satisfied. After such condition is satisfied, some of
the ranks will have converged. The ranking function is
modified to take into account these converged ranks so
as to reduce the ranking function's computation cost.
The modified ranking function is then solved until a
second stability condition is satisfied. After such
condition is satisfied more of the ranks will have
converged. The ranking function is again modified and
process continues until complete.",
acknowledgement = ack-nhfb,
}
@Article{Kamvar:2004:AMC,
author = "Sepandar Kamvar and Taher Haveliwala and Gene Golub",
title = "Adaptive methods for the computation of {PageRank}",
journal = j-LINEAR-ALGEBRA-APPL,
volume = "386",
number = "1",
pages = "51--65",
day = "15",
month = jul,
year = "2004",
CODEN = "LAAPAW",
DOI = "https://doi.org/10.1016/j.laa.2003.12.008",
ISSN = "0024-3795 (print), 1873-1856 (electronic)",
ISSN-L = "0024-3795",
MRclass = "60-04 (60G50 60J10)",
MRnumber = "MR2066607",
bibdate = "Tue Nov 9 07:02:36 MST 2004",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/00243795",
URL = "https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
https://www.math.utah.edu/pub/tex/bib/linala2000.bib",
ZMnumber = "1091.68044",
acknowledgement = ack-nhfb,
fjournal = "Linear Algebra and its Applications",
journal-URL = "http://www.sciencedirect.com/science/journal/00243795",
keywords = "Google search engine; PageRank algorithm",
}
@Article{Langville:2004:DIP,
author = "Amy N. Langville and Carl D. Meyer",
title = "Deeper inside {PageRank}",
journal = j-INTERNET-MATH,
volume = "1",
number = "3",
pages = "335--380",
year = "2004",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68U35",
MRnumber = "MR2111012",
bibdate = "Wed May 5 19:27:49 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1109190965",
ZMnumber = "1098.68010",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Langville:2004:UPI,
author = "Amy Nicole Langville and Carl Dean Meyer",
editor = "{ACM}",
booktitle = "{Proceedings of the 13th international World Wide Web
conference: Alternate track papers \& posters}",
title = "Updating {PageRank} with iterative aggregation",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "392--393",
year = "2004",
DOI = "https://doi.org/10.1137/1031050",
ISBN = "1-58113-912-8",
ISBN-13 = "978-1-58113-912-9",
LCCN = "????",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We present an algorithm for updating the PageRank
vector [1]. Due to the scale of the web, Google only
updates its famous PageRank vector on a monthly basis.
However, the Web changes much more frequently.
Drastically speeding the PageRank computation can lead
to fresher, more accurate rankings of the webpages
retrieved by search engines. It can also make the goal
of real-time personalized rankings within reach. On two
small subsets of the web, our algorithm updates
PageRank using just 25\% and 14\%, respectively, of the
time required by the original PageRank algorithm. Our
algorithm uses iterative aggregation techniques [7, 8]
to focus on the slow-converging states of the Markov
chain. The most exciting feature of this algorithm is
that it can be joined with other PageRank acceleration
methods, such as the dangling node lumpability
algorithm [6], quadratic extrapolation [4], and
adaptive PageRank [3], to realize even greater speedups
(potentially a factor of 60 or more speedup when all
algorithms are combined). every few weeks. Our solution
harnesses the power of iterative aggregation principles
for Markov chains to allow for much more frequent
updates to the valuable ranking vectors.",
acknowledgement = ack-nhfb,
keywords = "aggregation; disaggregation; link analysis; Markov
chains; pagerank; power method; stationary vector;
updating",
}
@InProceedings{Manaskasemsak:2004:PPC,
author = "Bundit Manaskasemsak and Arnon Rungsawang",
title = "Parallel {PageRank} computation on a gigabit {PC}
cluster",
crossref = "Barolli:2004:ICA",
volume = "1",
pages = "273--277",
year = "2004",
DOI = "https://doi.org/10.1109/AINA.2004.1283923",
bibdate = "Fri Feb 19 18:16:05 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1283923",
abstract = "Efficient computing the PageRank scores for a large
web graph is actually one of the hot issues in Web-IR
community. Recent researches propose to accelerate the
computation, both in algorithmic and architectural
ways. We here focus on a parallel PageRank
computational architecture on a cluster of Opteron PCs
networked via a Gigabit Ethernet. We propose both an
efficient parallel algorithm of the standard PageRank
computation, and a simple pairwise communication model
needed to synchronize local PageRank scores between
processors. Our experimental results conducted on a
large web graph, over 1.5 billion links, synthesized
from the real set of crawled web pages in the TH
domain, are quite promising. The current implementation
takes less than15 seconds for an iteration run.",
acknowledgement = ack-nhfb,
}
@Article{Markarian:2004:IEN,
author = "Roberto Markarian and Nelson M{\"o}ller",
title = "The importance of each node in a structure of links:
{Google PageRank}",
journal = "Bol. Asoc. Mat. Venez.",
volume = "11",
number = "2",
pages = "233--252",
year = "2004",
CODEN = "????",
ISSN = "1315-4125",
MRclass = "68U35 (15A18)",
MRnumber = "MR2139430",
bibdate = "Wed May 5 19:27:59 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1086.68658",
acknowledgement = ack-nhfb,
fjournal = "Bolet\'\i n de la Asociaci\'on Matem\'atica
Venezolana",
}
@InProceedings{Meng:2004:ELA,
author = "Tao Meng and Hongfei Yan and Jimin Wang and Xiaoming
Li",
title = "The Evolution of Link-Attributes for Pages and Its
Implications on Web Crawling",
crossref = "Zhong:2004:IWS",
pages = "578--581",
year = "2004",
bibdate = "Thu May 06 14:14:46 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Mihalcea:2004:PSN,
author = "Rada Mihalcea and Paul Tarau and Elizabeth Figa",
editor = "Margaret King and others",
booktitle = "{Coling Geneva 2004: 20th International Conference on
Computational Linguistics, August 23rd to 27th, 2004:
proceedings}",
title = "{PageRank} on semantic networks, with application to
word sense disambiguation",
publisher = "Association for Computational Linguistics",
address = "Morristown, NJ, USA",
pages = "??--??",
year = "2004",
DOI = "https://doi.org/10.3115/1220355.1220517",
ISBN = "1-932432-48-5",
ISBN-13 = "978-1-932432-48-0",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.cs.unt.edu/~rada/papers/mihalcea.coling04.pdf",
abstract = "This paper presents a new open text word sense
disambiguation method that combines the use of logical
inferences with PageRank-style algorithms applied on
graphs extracted from natural language documents. We
evaluate the accuracy of the proposed algorithm on
several sense-annotated texts, and show that it
consistently outperforms the accuracy of other
previously proposed knowledge-based word sense
disambiguation methods. We also explore and evaluate
methods that combine several open-text word sense
disambiguation algorithms.",
acknowledgement = ack-nhfb,
bookpages = "xvi + 763",
pagecount = "7",
}
@InProceedings{Suzuki:2004:HDP,
author = "K. Suzuki",
title = "How does propagational investment currency system
change the world?",
crossref = "IEEE:2004:SWI",
pages = "9--15",
year = "2004",
DOI = "https://doi.org/10.1109/SAINTW.2004.1268559",
bibdate = "Thu May 06 14:02:50 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Upstill:2003:PFF,
author = "Trystan Upstill and Nick Craswell and David Hawking",
editor = "????",
booktitle = "{Proceedings of the 8th Australasian Document
Computing Symposium, Canberra, Australia, December 15,
2003 (ADCS 2003)}",
title = "Predicting fame and fortune: {PageRank} or
{Indegree}?",
publisher = "????",
address = "????",
pages = "31--40",
month = dec,
year = "2003",
bibdate = "Mon Jul 08 08:43:33 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Wang:2004:CPD,
author = "Yuan Wang and David J. DeWitt",
editor = "Mario A. Nascimento",
booktitle = "{Proceedings of the thirtieth International Conference
on Very Large Data Bases: Toronto, Canada, August
31--September 3, 2004}",
title = "Computing {PageRank} in a distributed {Internet}
search system",
volume = "30",
publisher = pub-MORGAN-KAUFMANN,
address = pub-MORGAN-KAUFMANN:adr,
pages = "420--431",
year = "2004",
DOI = "https://doi.org/10.1145/383059.383071",
ISBN = "0-12-088469-0",
ISBN-13 = "978-0-12-088469-8",
LCCN = "QA76.9.D3 I559 2004",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Existing Internet search engines use web crawlers to
download data from the Web. Page quality is measured on
central servers, where user queries are also processed.
This paper argues that using crawlers has a list of
disadvantages. Most importantly, crawlers do not scale.
Even Google, the leading search engine, indexes less
than 1\% of the entire Web. This paper proposes a
distributed search engine framework, in which every web
server answers queries over its own data. Results from
multiple web servers will be merged to generate a
ranked hyperlink list on the submitting server. This
paper presents a series of algorithms that compute
PageRank in such framework. The preliminary experiments
on a real data set demonstrate that the system achieves
comparable accuracy on PageRank vectors to Google's
well-known PageRank algorithm and, therefore, high
quality of query results.",
acknowledgement = ack-nhfb,
}
@InProceedings{Xing:2004:WPA,
author = "W. Xing and A. Ghorbani",
booktitle = "{Proceedings of the Second Annual Conference on
Communication Networks and Services Research (2004)}",
title = "Weighted {PageRank} Algorithm",
crossref = "Ghorbani:2004:PAC",
pages = "305--314",
year = "2004",
DOI = "https://doi.org/10.1109/DNSR.2004.1344743",
ISBN = "0-7695-2096-0",
ISBN-13 = "978-0-7695-2096-4",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1344743",
abstract = "With the rapid growth of the Web, users get easily
lost in the rich hyper structure. Providing relevant
information to the users to cater to their needs is the
primary goal of website owners. Therefore, finding the
content of the Web and retrieving the users' interests
and needs from their behavior have become increasingly
important. Web mining is used to categorize users and
pages by analyzing the users' behavior,the content of
the pages, and the order of the URLs that tend to be
accessed in order. Web structure mining plays an
important role in this approach. Two page ranking
algorithms, HITS and PageRank, are commonly used in web
structure mining. Both algorithms treat all links
equally when distributing rank scores. Several
algorithms have been developed to improve the
performance of these methods. The Weighted PageRank
algorithm (WPR), an extension to the standard PageRank
algorithm, is introduced in this paper. WPR takes into
account the importance of both the inlinks and the
outlinks of the pages and distributes rank scores based
on the popularity of the pages. The results of our
simulation studies show that WPR performs better than
the conventional PageRank algorithm in terms of
returning larger number of relevant pages to a given
query.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9316",
keywords = "HITS; PageRank; Web Mining; Web Structure Mining;
Weighted PageRank",
}
@InProceedings{Yamamoto:2004:DPD,
author = "A. Yamamoto and D. Asahara and T. Itao and S. Tanaka
and T. Suda",
title = "Distributed {PageRank}: a distributed reputation model
for open peer-to-peer network",
crossref = "IEEE:2004:SWI",
pages = "389--394",
year = "2004",
DOI = "https://doi.org/10.1109/SAINTW.2004.1268664",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1268664",
abstract = "This paper proposes a distributed reputation model for
open peer-to-peer networks called distributed pagerank.
This model is motivated by the observation that
although pagerank has already satisfied the
requirements of reputation models, the centralized
calculation of pagerank is incompatible with
peer-to-peer networks. Distributed pagerank is a
decentralized approach for calculating the pagerank of
each peer by its reputation, in which the relationship
between peers is introduced as the equivalent to the
link between web pages. The distributed calculation of
pagerank is performed asynchronously by each peer as it
communicates with the other peers. The asynchronous
calculation accomplishes both demanding no extra
messages for the calculation of pagerank and steadily
calculating an accurate pagerank of each peer even
under the dynamic topology of relationships. The result
of the simulation has indicated that the calculated
pagerank value of each peer converges at the original
pagerank value under the static topology of
relationships, which is presumable under a dynamic
topology. A fully implemented application of
distributed pagerank has also been presented, which
supports dynamic formation of communities with
reputation ranking.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8957",
}
@InBook{Altman:2005:RSPa,
editor = "Alon Altman",
title = "Ranking systems: the {PageRank} axioms",
publisher = "International Begegnungs- und Forschungszentrum
f{\"u}r Informatik",
address = "Wadern, Germany",
pages = "??--??",
year = "2005",
ISBN = "????",
ISBN-13 = "????",
LCCN = "????",
bibdate = "Fri Jun 3 10:03:23 MDT 2011",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Dagstuhl seminar proceedings 05011",
URL = "http://drops.dagstuhl.de/opus/volltexte/2005/197/pdf/05011.AltmanAlon.Paper",
acknowledgement = ack-nhfb,
}
@InProceedings{Altman:2005:RSPb,
author = "A. Altman and M. Tennenholtz",
booktitle = "{EC '05: proceedings of the 6th ACM Conference on
Electronic Commerce, Vancouver, Canada, June 5--8,
2005}",
title = "Ranking systems: the {PageRank} axioms",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "1--8",
year = "2005",
ISBN = "1-59593-049-3",
ISBN-13 = "978-1-59593-049-1",
LCCN = "HF5548.32 .A26 2005",
bibdate = "Tue Jul 20 16:00:08 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
bookpages = "viii + 294",
keywords = "PageRank",
}
@InProceedings{Benczur:2005:FLR,
author = "Andr{\'a}s A. Bencz{\'u}r and K{\'a}roly Csalog{\'a}ny
and Tam{\'a}s Sarl{\'o}s",
editor = "{ACM}",
booktitle = "{Special interest tracks and posters of the 14th
international conference on World Wide Web}",
title = "On the feasibility of low-rank approximation for
personalized {PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "972--973",
year = "2005",
DOI = "https://doi.org/10.1145/1062745.1062824",
ISBN = "1-59593-051-5",
ISBN-13 = "978-1-59593-051-4",
LCCN = "????",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Personalized PageRank expresses backlink-based page
quality around user-selected pages in a similar way to
PageRank over the entire Web. Algorithms for computing
personalized PageRank on the fly are either limited to
a restricted choice of page selection or believed to
behave well only on sparser regions of the Web. In this
paper we show the feasibility of computing personalized
PageRank by a $ k < 1000 $ low-rank approximation of
the Page-Rank transition matrix; by our algorithm we
may compute an approximate personalized Page-Rank by
multiplying an $ n \times k $, a $ k \times n $ matrix
and the $n$-dimensional personalization vector. Since
low-rank approximations are accurate on dense regions,
we hope that our technique will combine well with known
algorithms.",
acknowledgement = ack-nhfb,
keywords = "link analysis; low-rank approximation; personalized
PageRank; singular value decomposition; web information
retrieval",
}
@Article{Berkhin:2005:SPC,
author = "Pavel Berkhin",
title = "A survey on {PageRank} computing",
journal = j-INTERNET-MATH,
volume = "2",
number = "1",
pages = "73--120",
year = "2005",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68U35",
MRnumber = "MR2166277 (2006c:68180)",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1128530802",
ZMnumber = "1100.68504",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@Book{Berry:2005:USE,
author = "Michael W. Berry and Murray Browne",
title = "Understanding search engines: mathematical modeling
and text retrieval",
publisher = pub-SIAM,
address = pub-SIAM:adr,
edition = "Second",
pages = "xvii + 117",
year = "2005",
ISBN = "0-89871-581-4",
ISBN-13 = "978-0-89871-581-1",
LCCN = "TK5105.884 .B47 2005",
bibdate = "Fri Jun 3 10:03:23 MDT 2011",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
subject = "Web search engines; Vector spaces; Text processing
(Computer science)",
tableofcontents = "Preface to the Second Edition \\
1. Introduction \\
1.1. Document file preparation \\
1.1.1. Manual indexing \\
1.1.2. File cleanup \\
1.2. Information extraction \\
1.3. Vector space modeling \\
1.4. Matrix decompositions \\
1.5. Query representations \\
1.6. Ranking and relevance feedback \\
1.7. Searching by link structure \\
1.8. User interface \\
1.9. Book format \\
2. Document file preparation \\
2.1. Document purification and analysis \\
2.1.1. Text formatting \\
2.1.2. Validation \\
2.2. Manual indexing \\
2.3. Automatic indexing \\
2.4. Item normalization \\
2.5. Inverted file structures \\
2.5.1. Document file \\
2.5.2. Dictionary list \\
2.5.3. Inversion list \\
2.5.4. Other file structures \\
3. Vector space models \\
3.1. Construction \\
3.1.1. Term-by-document matrices \\
3.1.2. Simple Query matching \\
3.2. Design issues \\
3.2.1. Term weighting \\
3.2.2. Sparse matrix storage \\
3.2.3. Low-rank approximations \\
4. Matrix decompositions \\
4.1. QR factorization \\
4.2. Singular value decomposition \\
4.2.1. Low-rank approximations \\
4.2.2. Query matching \\
4.2.3. Software \\
4.3. Semidiscrete decomposition \\
4.4. Updating techniques \\
5. Query management \\
5.1. Query binding \\
5.2. Types of queries \\
5.2.1. Boolean queries \\
5.2.2. Natural language queries \\
5.2.3. Thesaurus queries \\
5.2.4. Fuzzy queries \\
5.2.5. Term searches \\
5.2.6. Probabilistic queries \\
6. Ranking and relevance feedback \\
6.1. Performance evaluation \\
6.1.1. Precision \\
6.1.2. Recall \\
6.1.3. Average precision \\
6.1.4. Genetic algorithms \\
6.2. Relevance feedback \\
7. Searching by link structure \\
7.1. HITS method \\
7.1.1. HITS implementation \\
7.1.2. HITS summary \\
7.2. PageRank method \\
7.2.1. PageRank adjustments \\
7.2.2. PageRank implementation \\
7.2.3. PageRank summary \\
8. User interface considerations \\
8.1. General guidelines \\
8.2. Search engine interfaces \\
8.2.1. Form fill-in \\
8.2.2. Display considerations \\
8.2.3. Progress indication \\
8.2.4. No penalties for error \\
8.2.5. Results \\
8.2.6. Test and retest \\
8.2.7. Final considerations \\
9. Further reading \\
9.1. General textbooks on IR \\
9.2. Computational methods and software \\
9.3. Search engines \\
9.4. User interfaces \\
Bibliography \\
Index",
}
@Article{Bianchini:2005:IP,
author = "Monica Bianchini and Marco Gori and Franco Scarselli",
title = "Inside {PageRank}",
journal = j-TOIT,
volume = "5",
number = "1",
pages = "92--128",
month = feb,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1016/S0169-7552(98)00061-0",
ISSN = "1533-5399 (print), 1557-6051 (electronic)",
ISSN-L = "1533-5399",
bibdate = "Thu Apr 14 10:31:40 MDT 2005",
bibsource = "http://www.acm.org/pubs/contents/journals/toit/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/toit.bib",
abstract = "Although the interest of a Web page is strictly
related to its content and to the subjective readers'
cultural background, a measure of the page authority
can be provided that only depends on the topological
structure of the Web. PageRank is a noticeable way to
attach a score to Web pages on the basis of the Web
connectivity. In this article, we look inside PageRank
to disclose its fundamental properties concerning
stability, complexity of computational scheme, and
critical role of parameters involved in the
computation. Moreover, we introduce a circuit analysis
that allows us to understand the distribution of the
page score, the way different Web communities interact
each other, the role of dangling pages (pages with no
outlinks), and the secrets for promotion of Web
pages.",
acknowledgement = ack-nhfb,
fjournal = "ACM Transactions on Internet Technology (TOIT)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J780",
keywords = "Information retrieval; Markov chains; PageRank; search
engines; searching the Web; Web page scoring",
}
@Article{Boldi:2005:PEP,
author = "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
title = "Paradoxical effects in {PageRank} incremental
computations",
journal = j-INTERNET-MATH,
volume = "2",
number = "3",
pages = "387--404",
year = "2005",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68U35 (05C80 68R10)",
MRnumber = "MR2212371 (2006j:68129)",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1150474888",
ZMnumber = "1095.68503",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Boldi:2005:PFD,
author = "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceedings of
the 14th international conference on World Wide Web",
title = "{PageRank} as a function of the damping factor",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "557--566",
year = "2005",
DOI = "https://doi.org/10.1145/382979.383041",
ISBN = "1-59593-046-9",
ISBN-13 = "978-1-59593-046-0",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "PageRank is defined as the stationary state of a
Markov chain. The chain is obtained by perturbing the
transition matrix induced by a web graph with a damping
factor $ \alpha $ that spreads uniformly part of the
rank. The choice of $ \alpha $ is eminently empirical,
and in most cases the original suggestion $ \alpha $ =
0.85 by Brin and Page is still used. Recently, however,
the behaviour of PageRank with respect to changes in $
\alpha $ was discovered to be useful in link-spam
detection[21]. Moreover, an analytical justification of
the value chosen for $ \alpha $ is still missing. In
this paper, we give the first mathematical analysis of
PageRank when $ \alpha $ changes. In particular, we
show that, contrarily to popular belief, for real-world
graphs values of $ \alpha $ close to 1 do not give a
more meaningful ranking. Then, we give closed-form
formulae for PageRank derivatives of any order, and an
extension of the Power Method that approximates them
with convergence O (t k $ \alpha $ t ) for the k-th
derivative. Finally, we show a tight connection between
iterated computation and analytical behaviour by
proving that the k-th iteration of the Power Method
gives exactly the PageRank value obtained using a
Maclaurin polynomial of degree k. The latter result
paves the way towards the application of analytical
methods to the study of PageRank.",
acknowledgement = ack-nhfb,
keywords = "approximation; PageRank; Web graph",
}
@InProceedings{Boldi:2005:TRD,
author = "P. Boldi",
booktitle = "Poster Proceedings of the 14th International
Conference on the World Wide Web (WWW2005)",
title = "TotalRank: Ranking without damping",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "898--899",
year = "2005",
bibdate = "Tue Aug 11 17:28:42 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Article{Brezinski:2005:EMP,
author = "Claude Brezinski and Michela Redivo-Zaglia and Stefano
Serra-Capizzano",
title = "Extrapolation methods for {PageRank} computations",
journal = j-C-R-MATH-ACAD-SCI-PARIS,
volume = "340",
number = "5",
pages = "393--397",
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.crma.2005.01.015",
ISSN = "1631-073X",
ISSN-L = "1631-073X",
MRclass = "65F15",
MRnumber = "MR2127117",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1066.65040",
acknowledgement = ack-nhfb,
fjournal = "Comptes Rendus Math\'ematique. Acad\'emie des
Sciences. Paris",
}
@InProceedings{daCosta:2005:WSM,
author = "M. G. {da Costa, Jr.} and Zhiguo Gong",
title = "{Web} structure mining: an introduction",
crossref = "Meng:2005:IIC",
pages = "??--??",
year = "2005",
DOI = "https://doi.org/10.1109/ICIA.2005.1635156",
bibdate = "Thu May 06 15:33:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@TechReport{DelCorso:2005:CKS,
author = "Gianna M. {Del Corso} and Antonio Gull{\'\i} and
Francesco Romani",
title = "Comparison of {Krylov} Subspace Methods on the
{PageRank} Problem",
type = "Technical Report",
number = "TR-05-20",
institution = "University of Pisa",
address = "Pisa, Italy",
pages = "????",
year = "2005",
bibdate = "Wed Nov 30 08:06:39 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Article{DelCorso:2005:FPC,
author = "Gianna M. {Del Corso} and Antonio Gull{\'\i} and
Francesco Romani",
title = "Fast {PageRank} computation via a sparse linear
system",
journal = j-INTERNET-MATH,
volume = "2",
number = "3",
pages = "251--273",
year = "2005",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68U35 (05C80 65F50)",
MRnumber = "MR2212366 (2006j:68131)",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1150474883",
ZMnumber = "1095.68578",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@Article{Dominich:2005:PII,
author = "S{\'a}ndor Dominich and Adrienn Skrop",
title = "{PageRank} and Interaction Information Retrieval:
Research Articles",
journal = "Journal of the American Society for Information
Science and Technology",
volume = "56",
number = "1",
pages = "63--69",
month = jan,
year = "2005",
CODEN = "JASIEF",
DOI = "https://doi.org/10.1002/asi.v56:1",
ISSN = "1532-2882 (print), 1532-2890 (electronic)",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "The PageRank method is used by the Google Web search
engine to compute the importance of Web pages. Two
different views have been developed for the
interpretation of the PageRank method and values: (a)
stochastic (random surfer): the PageRank values can be
conceived as the steady-state distribution of a Markov
chain, and (b) algebraic: the PageRank values form the
eigenvector corresponding to eigenvalue 1 of the Web
link matrix. The Interaction Information Retrieval (I 2
R) method is a nonclassical information retrieval
paradigm, which represents a connectionist approach
based on dynamic systems. In the present paper, a
different interpretation of PageRank is proposed,
namely, a dynamic systems viewpoint, by showing that
the PageRank method can be formally interpreted as a
particular case of the Interaction Information
Retrieval method; and thus, the PageRank values may be
interpreted as neutral equilibrium points of the Web.",
acknowledgement = ack-nhfb,
ajournal = "J. Am. Soc. Inf. Sci. Technol.",
fjournal = "Journal of the American Society for Information
Science and Technology",
}
@InProceedings{Eirinaki:2005:UBP,
author = "Magdalini Eirinaki and Michalis Vazirgiannis",
title = "Usage-based {PageRank} for {Web} personalization",
crossref = "Han:2005:FII",
pages = "130--137",
year = "2005",
DOI = "https://doi.org/10.1109/ICDM.2005.148",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1565671",
abstract = "Recommendation algorithms aim at proposing 'next'
pages to a user based on her current visit and the past
users' navigational patterns. In the vast majority of
related algorithms, only the usage data are used to
produce recommendations, whereas the structural
properties of the Web graph are ignored. We claim that
taking also into account the web structure and using
link analysis algorithms ameliorates the quality of
recommendations. In this paper we present UPR, a novel
personalization algorithm which combines usage data and
link analysis techniques for ranking and recommending
web pages to the end user. Using the web site's
structure and its usage data we produce personalized
navigational graph synopses (prNG) to be used for
applying UPR and produce personalized recommendations.
Experimental results show that the accuracy of the
recommendations is superior to pure usage-based
approaches.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10470",
}
@Article{Farahat:2005:ARH,
author = "Ayman Farahat and Thomas LoFaro and Joel C. Miller and
Gregory Rae and Lesley A. Ward",
title = "Authority Rankings from {HITS}, {PageRank}, and
{SALSA}: Existence, Uniqueness, and Effect of
Initialization",
journal = j-SIAM-J-SCI-COMP,
volume = "27",
number = "4",
pages = "1181--1201",
month = jul,
year = "2005",
CODEN = "SJOCE3",
DOI = "https://doi.org/10.1137/S1064827502412875",
ISSN = "1064-8275 (print), 1095-7197 (electronic)",
ISSN-L = "1064-8275",
MRclass = "68U35 (15A18 15A48 68R10 68W40)",
MRnumber = "MR2199745 (2006m:68169)",
MRreviewer = "Mirel Co{\c{s}}ulschi",
bibdate = "Tue Jun 27 09:24:24 MDT 2006",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SISC/27/4;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://epubs.siam.org/volume-27/art_41287.html",
ZMnumber = "1094.68111",
abstract = "Algorithms such as Kleinberg's HITS algorithm, the
PageRank algorithm of Brin and Page, and the SALSA
algorithm of Lempel and Moran use the link structure of
a network of web pages to assign weights to each page
in the network. The weights can then be used to rank
the pages as authoritative sources. These algorithms
share a common underpinning; they find a dominant
eigenvector of a nonnegative matrix that describes the
link structure of the given network and use the entries
of this eigenvector as the page weights. We use this
commonality to give a unified treatment, proving the
existence of the required eigenvector for the PageRank,
HITS, and SALSA algorithms, the uniqueness of the
PageRank eigenvector, and the convergence of the
algorithms to these eigenvectors. However, we show that
the HITS and SALSA eigenvectors need not be unique. We
examine how the initialization of the algorithms
affects the final weightings produced. We give examples
of networks that lead the HITS and SALSA algorithms to
return nonunique or nonintuitive rankings. We
characterize all such networks in terms of the
connectivity of the related HITS authority graph. We
propose a modification, Exponentiated Input to HITS, to
the adjacency matrix input to the HITS algorithm. We
prove that Exponentiated Input to HITS returns a unique
ranking, provided that the network is weakly connected.
Our examples also show that SALSA can give inconsistent
hub and authority weights, due to nonuniqueness. We
also mention a small modification to the SALSA
initialization which makes the hub and authority
weights consistent.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Scientific Computing",
journal-URL = "http://epubs.siam.org/sisc",
}
@Article{Fogaras:2005:TSF,
author = "D{\'a}niel Fogaras and Bal{\'a}zs R{\'a}cz and
K{\'a}roly Csalog{\'a}ny and Tam{\'a}s Sarl{\'o}s",
title = "Towards scaling fully personalized {PageRank}:
algorithms, lower bounds, and experiments",
journal = j-INTERNET-MATH,
volume = "2",
number = "3",
pages = "333--358",
year = "2005",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68U35 (05C80 68R10)",
MRnumber = "MR2212369 (2006j:68132)",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1150474886",
ZMnumber = "1095.68579",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@Article{Gori:2005:EAG,
author = "M. Gori and M. Maggini and L. Sarti",
title = "Exact and approximate graph matching using random
walks",
journal = j-IEEE-TRANS-PATT-ANAL-MACH-INTEL,
volume = "27",
number = "7",
pages = "1100--1111",
month = jul,
year = "2005",
CODEN = "ITPIDJ",
DOI = "https://doi.org/10.1109/TPAMI.2005.138",
ISSN = "0162-8828",
ISSN-L = "0162-8828",
bibdate = "Thu May 06 14:59:25 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE Transactions on Pattern Analysis and Machine
Intelligence",
journal-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34",
}
@Article{Higham:2005:GPM,
author = "Desmond J. Higham",
title = "{Google PageRank} as mean playing time for pinball on
the reverse web",
journal = j-APPL-MATH-LETT,
volume = "18",
number = "12",
pages = "1359--1362",
year = "2005",
CODEN = "AMLEEL",
DOI = "https://doi.org/10.1016/j.aml.2005.02.020",
ISSN = "0893-9659 (print), 1873-5452 (electronic)",
ISSN-L = "0893-9659",
MRclass = "68U35 (60J10 60J20)",
MRnumber = "MR2189889",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1083.68509",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics Letters. An International Journal
of Rapid Publication",
journal-URL = "http://www.sciencedirect.com/science/journal/08939659",
}
@Article{Ipsen:2005:CAP,
author = "Ilse C. F. Ipsen and Steve Kirkland",
title = "Convergence Analysis of a {PageRank} Updating
Algorithm by {Langville} and {Meyer}",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "27",
number = "4",
pages = "952--967",
year = "2005",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/S0895479804439808",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "The PageRank updating algorithm proposed by Langville
and Meyer is a special case of an iterative
aggregation/disaggregation (SIAD) method for computing
stationary distributions of very large Markov chains.
It is designed, in particular, to speed up the
determination of PageRank, which is used by the search
engine Google in the ranking of web pages. In this
paper the convergence, in exact arithmetic, of the SIAD
method is analyzed. The SIAD method is expressed as the
power method preconditioned by a partial LU
factorization. This leads to a simple derivation of the
asymptotic convergence rate of the SIAD method. It is
known that the power method applied to the Google
matrix always converges, and we show that the
asymptotic convergence rate of the SIAD method is at
least as good as that of the power method. Furthermore,
by exploiting the hyperlink structure of the web it can
be shown that the asymptotic convergence rate of the
SIAD method applied to the Google matrix can be made
strictly faster than that of the power method.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
keywords = "aggregation/disaggregation; Google; Markov chain;
PageRank; power method; stochastic complement",
}
@InProceedings{Kolda:2005:HOW,
author = "T. G. Kolda and B. W. Bader and J. P. Kenny",
title = "Higher-order {Web} link analysis using multilinear
algebra",
crossref = "Han:2005:FII",
pages = "??--??",
year = "2005",
DOI = "https://doi.org/10.1109/ICDM.2005.77",
bibdate = "Thu May 06 15:45:35 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Linear algebra is a powerful and proven tool in Web
search. Techniques, such as the PageRank algorithm of
Brin and Page and the HITS algorithm of Kleinberg,
score Web pages based on the principal eigenvector (or
singular vector) of a particular non-negative matrix
that captures the hyperlink structure of the Web graph.
We propose and test a new methodology that uses
multilinear algebra to elicit more information from a
higher-order representation of the hyperlink graph. We
start by labeling the edges in our graph with the
anchor text of the hyperlinks so that the associated
linear algebra representation is a sparse, three-way
tensor. The first two dimensions of the tensor
represent the Web pages while the third dimension adds
the anchor text. We then use the rank-1 factors of a
multilinear PARAFAC tensor decomposition, which are
akin to singular vectors of the SVD, to automatically
identify topics in the collection along with the
associated authoritative Web pages.",
acknowledgement = ack-nhfb,
pagecount = "8",
}
@InProceedings{Kurland:2005:PHS,
author = "Oren Kurland and Lillian Lee",
editor = "{ACM}",
booktitle = "Proceedings of the 28th annual international ACM SIGIR
conference on Research and development in information
retrieval",
title = "{PageRank} without hyperlinks: structural re-ranking
using links induced by language models",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "306--313",
year = "2005",
DOI = "https://doi.org/10.1145/383952.384019",
ISBN = "1-59593-034-5",
ISBN-13 = "978-1-59593-034-7",
LCCN = "????",
bibdate = "Sat May 8 18:33:07 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Inspired by the PageRank and HITS (hubs and
authorities) algorithms for Web search, we propose a
structural re-ranking approach to ad hoc information
retrieval: we reorder the documents in an initially
retrieved set by exploiting asymmetric relationships
between them. Specifically, we consider generation
links, which indicate that the language model induced
from one document assigns high probability to the text
of another; in doing so, we take care to prevent bias
against long documents. We study a number of re-ranking
criteria based on measures of centrality in the graphs
formed by generation links, and show that integrating
centrality into standard language-model-based retrieval
is quite effective at improving precision at top
ranks.",
acknowledgement = ack-nhfb,
keywords = "authorities; graph-based retrieval; high-accuracy
retrieval; HITS; hubs; language modeling; PageRank;
social networks; structural re-ranking",
}
@Article{Langville:2005:RPP,
author = "Amy N. Langville and Carl D. Meyer",
title = "A Reordering for the {PageRank} Problem",
journal = j-SIAM-J-SCI-COMP,
volume = "27",
number = "6",
pages = "2112--2120",
month = nov,
year = "2005",
CODEN = "SJOCE3",
DOI = "https://doi.org/10.1137/040607551",
ISSN = "1064-8275 (print), 1095-7197 (electronic)",
ISSN-L = "1064-8275",
MRclass = "68U35 (65F30); 65F30 65C40 60J22 65F50",
MRnumber = "MR2211442 (2006k:68167)",
bibdate = "Tue Jun 27 09:24:29 MDT 2006",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SISC/27/6;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://epubs.siam.org/volume-27/art_60755.html;
https://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib",
ZMnumber = "1103.65048",
abstract = "We describe a reordering particularly suited to the
PageRank problem, which reduces the computation of the
PageRank vector to that of solving a much smaller
system and then using forward substitution to get the
full solution vector. We compare the theoretical rates
of convergence of the original PageRank algorithm to
that of the new reordered PageRank algorithm, showing
that the new algorithm can do no worse than the
original algorithm. We present results of an
experimental comparison on five datasets, which
demonstrate that the reordered PageRank algorithm can
provide a speedup of as much as a factor of 6. We also
note potential additional benefits that result from the
proposed reordering.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Scientific Computing",
journal-URL = "http://epubs.siam.org/sisc",
}
@Article{Langville:2005:UMC,
author = "Amy N. Langville and Carl D. Meyer",
title = "Updating {Markov} Chains with an Eye on {Google}'s
{PageRank}",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "27",
number = "4",
pages = "968--987",
year = "2005",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/040619028",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "An iterative algorithm based on
aggregation/disaggregation principles is presented for
updating the stationary distribution of a finite
homogeneous irreducible Markov chain. The focus is on
large-scale problems of the kind that are characterized
by Google's PageRank application, but the algorithm is
shown to work well in general contexts. The algorithm
is flexible in that it allows for changes to the
transition probabilities as well as for the creation or
deletion of states. In addition to establishing the
rate of convergence, it is proven that the algorithm is
globally convergent. Results of numerical experiments
are presented.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
keywords = "aggregation/disaggregation; Google; Markov chains;
PageRank; stationary vector; stochastic
complementation; updating",
}
@InProceedings{Liu:2005:WIA,
author = "Tie-Yan Liu and Wei-Ying Ma",
title = "Webpage importance analysis using conditional {Markov}
random walk",
crossref = "Skowron:2005:PIW",
pages = "515--521",
year = "2005",
DOI = "https://doi.org/10.1109/WI.2005.161",
bibdate = "Thu May 06 16:39:05 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Manaskasemsak:2005:EPB,
author = "Bundit Manaskasemsak and Arnon Rungsawang",
booktitle = "{Proceedings of the 11th International Conference on
Parallel and Distributed Systems (2005)}",
title = "An efficient partition-based parallel {PageRank}
algorithm",
crossref = "Barolli:2005:ICP",
volume = "1",
pages = "257--263 Vol. 1",
year = "2005",
DOI = "https://doi.org/10.1109/ICPADS.2005.85",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1531136",
abstract = "PageRank becomes the most well-known re-ranking
technique of the search results. By its iterative
computational nature, the computation takes much
computing time and resource. Researchers have then
devoted much attention in studying an efficient way to
compute the PageRank scores of a very large web graph.
However, only a few of them focus on large-scale
PageRank computation using parallel processing
techniques. In this paper, we propose a Partition-based
parallel PageRank algorithm that can efficiently run on
a low-cost parallel environment like the PC cluster.
For comparison, we also study the other two known
techniques, as well as propose an analytical discussion
concerning I/O and synchronization cost, and memory
usage. Experimental results with two web graphs
synthesized from the {\tt .TH} domain and the Stanford
WebBase project are very promising.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10248",
}
@InProceedings{Martins:2005:GRA,
author = "B. Martins and M. J. Silva",
title = "A graph-ranking algorithm for geo-referencing
documents",
crossref = "Han:2005:FII",
pages = "??--??",
year = "2005",
DOI = "https://doi.org/10.1109/ICDM.2005.6",
bibdate = "Thu May 06 15:33:58 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
pagecount = "4",
}
@PhdThesis{Mason:2005:DCP,
author = "Kahn Mason",
title = "Detecting colluders in {PageRank} finding slow mixing
states in a {Markov} chain",
type = "Thesis ({Ph.D.})",
school = "Stanford University",
address = "Stanford, CA, USA",
pages = "75",
year = "2005",
ISBN = "0-542-29567-9",
ISBN-13 = "978-0-542-29567-6",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "Order number AAI3187317.",
URL = "http://wwwlib.umi.com/dissertations/fullcit/3187317",
abstract = "The PageRank algorithm evaluates webpage reputations
based on the hyperlinks that connect them. Webpages
that collude to boost their reputations significantly
distort the resulting rankings. We introduce a measure
for assessing the degree to which a set of webpages
boosts its reputation. There is no known efficient
algorithm that is guaranteed to detect significantly
boosted sets when they exist. However, we provide
metrics that, under reasonable conditions, are
guaranteed to detect a member of a significantly
boosted set, if one exists, and address various
implementation issues that arise in incorporating these
metrics into PageRank.",
acknowledgement = ack-nhfb,
advisor = "Benjamin Van Roy",
}
@InProceedings{Massa:2005:PRU,
author = "P. Massa and C. Hayes",
title = "{Page-reRank}: using trusted links to re-rank
authority",
crossref = "Skowron:2005:PIW",
pages = "614--617",
year = "2005",
DOI = "https://doi.org/10.1109/WI.2005.112",
bibdate = "Thu May 06 16:22:59 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{McSherry:2005:UAA,
author = "Frank McSherry",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceedings of
the 14th international conference on World Wide Web",
title = "A uniform approach to accelerated {PageRank}
computation",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "575--582",
year = "2005",
DOI = "https://doi.org/10.1145/775152.775191",
ISBN = "1-59593-046-9",
ISBN-13 = "978-1-59593-046-0",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In this note we consider a simple reformulation of the
traditional power iteration algorithm for computing the
stationary distribution of a Markov chain. Rather than
communicate their current probability values to their
neighbors at each step, nodes instead communicate only
changes in probability value. This reformulation
enables a large degree of flexibility in the manner in
which nodes update their values, leading to an array of
optimizations and features, including faster
convergence, efficient incremental updating, and a
robust distributed implementation.While the spirit of
many of these optimizations appear in previous
literature, we observe several cases where this
unification simplifies previous work, removing
technical complications and extending their range of
applicability. We implement and measure the performance
of several optimizations on a sizable (34M node) web
subgraph, seeing significant composite performance
gains, especially for the case of incremental
recomputation after changes to the web graph.",
acknowledgement = ack-nhfb,
keywords = "link analysis; PageRank; random walks; web graph",
}
@Article{Morrison:2005:GUS,
author = "Julie L. Morrison and Rainer Breitling and Desmond J.
Higham and David R. Gilbert",
title = "{GeneRank}: Using search engine technology for the
analysis of microarray experiments",
journal = j-BMC-BIOINFORMATICS,
volume = "6",
number = "??",
pages = "233--239",
month = "??",
year = "2005",
CODEN = "BBMIC4",
DOI = "https://doi.org/10.1186/1471-2105-6-233",
ISSN = "1471-2105",
ISSN-L = "1471-2105",
bibdate = "Tue Aug 11 17:28:42 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1261158/",
acknowledgement = ack-nhfb,
fjournal = "BMC Bioinformatics",
journal-URL = "http://www.biomedcentral.com/bmcbioinformatics/",
}
@InProceedings{Padmanabhan:2005:WWI,
author = "D. Padmanabhan and P. Desikan and J. Srivastava and K.
Riaz",
title = "{WICER}: a weighted inter-cluster edge ranking for
clustered graphs",
crossref = "Skowron:2005:PIW",
pages = "522--528",
year = "2005",
DOI = "https://doi.org/10.1109/WI.2005.166",
bibdate = "Thu May 06 16:37:10 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{Rungsawang:2005:PBP,
author = "Arnon Rungsawang and Bundit Manaskasemsak",
booktitle = "{ICITA 2005: Third International Conference on
Information Technology and Applications}",
title = "Partition-Based Parallel {PageRank} Algorithm",
crossref = "He:2005:TIC",
volume = "2",
pages = "57--62",
year = "2005",
DOI = "https://doi.org/10.1109/ICITA.2005.207",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1488928",
abstract = "A re-ranking technique, called 'PageRank' brings a
successful story behind the Google search engine. Many
studies focus on finding an efficient way to compute
the PageRank scores of a large web graph. Researchers
propose to compute them sequentially by reducing the
I/O cost of disk access, improving the convergence
rate, or even employing Peer-2-Peer architecture, etc.
However, only a few concentrate on computation using
parallel processing techniques. In this paper, we
propose a Partition-based parallel PageRank algorithm
that can be efficiently run on a low-cost parallel
environment like PC cluster. For comparison, we also
study other two well-known PageRank techniques, and
provide an analytical discussion of their performance
in terms of I/O and synchronization cost, as well as
memory usage. Experimental results show a promising
improvement on a large artificial web graph synthesized
from the {\tt .TH} domain.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9966",
}
@Article{Serra-Capizzano:2005:JCF,
author = "Stefano Serra-Capizzano",
title = "{Jordan} canonical form of the {Google} matrix: a
potential contribution to the {PageRank} computation",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "27",
number = "2",
pages = "305--312",
month = apr,
year = "2005",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/S0895479804441407",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
MRclass = "15A18 (15A21)",
MRnumber = "MR2179674 (2006g:15019)",
bibdate = "Thu Dec 29 16:33:54 MST 2005",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SIMAX/27/2;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "See comments \cite{Wu:2008:CJC}.",
URL = "http://epubs.siam.org/sam-bin/dbq/article/44140;
https://www.math.utah.edu/pub/tex/bib/siamjmatanaappl.bib",
ZMnumber = "1103.65051",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
}
@InProceedings{Tarau:2005:SDE,
author = "Paul Tarau and Rada Mihalcea and Elizabeth Figa",
editor = "{ACM}",
booktitle = "Proceedings of the 2005 ACM Symposium on Applied
computing",
title = "Semantic document engineering with {WordNet} and
{PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "782--786",
year = "2005",
DOI = "https://doi.org/10.3115/981658.981684",
ISBN = "1-58113-964-0",
ISBN-13 = "978-1-58113-964-8",
LCCN = "????",
bibdate = "Sat May 8 18:33:04 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "This paper describes Natural Language Processing
techniques for document engineering in combination with
graph algorithms and statistical methods. Google's
PageRank and similar fast-converging recursive graph
algorithms have provided practical means to statically
rank vertices of large graphs like the World Wide Web.
By combining a fast Java-based PageRank implementation
with a Prolog base inferential layer, running on top of
an optimized WordNet graph, we describe applications to
word sense disambiguation and evaluate their accuracy
on standard benchmarks.",
acknowledgement = ack-nhfb,
keywords = "logic programming; natural language processing;
PageRank-style graph algorithms; semantics-based
document processing; word sense disambiguation;
WordNet",
}
@InProceedings{Tummarello:2005:SAH,
author = "G. Tummarello and C. Morbidoni and P. Puliti and F.
Piazza",
title = "Semantic audio hyperlinking: a multimedia-semantic
{Web} scenario",
crossref = "Nesi:2005:FIC",
pages = "??--??",
year = "2005",
DOI = "https://doi.org/10.1109/AXMEDIS.2005.45",
bibdate = "Thu May 06 15:54:55 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Vigna:2005:TTP,
author = "Sebastiano Vigna",
editor = "Tatsuya Hagino and Allan Ellis",
booktitle = "{Special Interest Tracks and Posters of the 14th
International Conference on the World Wide Web, WWW 05.
Chiba, Japan, May 10--14, 2005}",
title = "{TruRank}: Taking {PageRank} to the limit",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "976--977",
year = "2005",
DOI = "https://doi.org/10.1145/1062745.1062826",
ISBN = "1-59593-051-5",
ISBN-13 = "978-1-59593-051-4",
LCCN = "TK5105.888 I573 2005",
bibdate = "Tue Aug 11 17:39:04 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
bookpages = "1192",
}
@InProceedings{Wang:2005:DP,
author = "Xuanhui Wang and Azadeh Shakery and Tao Tao",
editor = "{ACM}",
booktitle = "Annual ACM Conference on Research and Development in
Information Retrieval Proceedings of the 28th annual
international ACM SIGIR conference on Research and
development in information retrieval",
title = "{Dirichlet PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "661--662",
year = "2005",
DOI = "https://doi.org/10.1145/383952.384019",
ISBN = "1-59593-034-5",
ISBN-13 = "978-1-59593-034-7",
LCCN = "????",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "PageRank has been known to be a successful algorithm
in ranking web sources. In order to avoid the rank sink
problem, PageRank assumes that a surfer, being in a
page, jumps to a random page with a certain
probability. In the standard PageRank algorithm, the
jumping probabilities are assumed to be the same for
all the pages, regardless of the page properties. This
is not the case in the real world, since presumably a
surfer would more likely follow the out-links of a
high-quality hub page than follow the links of a
low-quality one. In this poster, we propose a novel
algorithm `Dirichlet PageRank' to address this problem
by adapting flexible jumping probabilities based on the
number of out-links in a page. Empirical results on
TREC data show that our method outperforms the standard
PageRank algorithm.",
acknowledgement = ack-nhfb,
}
@Article{Weingart:2005:IBU,
author = "Peter Weingart",
title = "Impact of bibliometrics upon the science system:
Inadvertent consequences?",
journal = j-SCIENTOMETRICS,
volume = "62",
number = "1",
pages = "117--131",
month = jan,
year = "2005",
CODEN = "SCNTDX",
DOI = "https://doi.org/10.1007/s11192-005-0007-7",
ISSN = "0138-9130 (print), 1588-2861 (electronic)",
ISSN-L = "0138-9130",
bibdate = "Thu Jun 02 08:35:18 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/article/10.1007/s11192-005-0007-7",
abstract = "Ranking of research institutions by bibliometric
methods is an improper tool for research performance
evaluation, even at the level of large institutions.
The problem, however, is not the ranking as such. The
indicators used for ranking are often not advanced
enough, and this situation is part of the broader
problem of the application of insufficiently developed
bibliometric indicators used by persons who do not have
clear competence and experience in the field of
quantitative studies of science. After a brief overview
of the basic elements of bibliometric analysis, I
discuss the major technical and methodological problems
in the application of publication and citation data in
the context of evaluation. Then I contend that the core
of the problem lies not necessarily at the side of the
data producer. Quite often persons responsible for
research performance evaluation, for instance
scientists themselves in their role as head of
institutions and departments, science administrators at
the government level and other policy makers show an
attitude that encourages `quick and dirty' bibliometric
analyses whereas better quality is available. Finally,
the necessary conditions for a successful application
of advanced bibliometric indicators as support tool for
peer review are discussed.",
acknowledgement = ack-nhfb,
fjournal = "Scientometrics",
journal-URL = "http://link.springer.com/journal/11192",
}
@InProceedings{Wu:2005:ULM,
author = "Jie Wu and K. Aberer",
title = "Using a Layered {Markov} Model for Distributed {Web}
Ranking Computation",
crossref = "IEEE:2005:ICD",
pages = "533--542",
year = "2005",
DOI = "https://doi.org/10.1109/ICDCS.2005.84",
bibdate = "Thu May 06 15:11:10 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Yang:2005:RMW,
author = "Christopher C. Yang and K. Y. Chan",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Special
interest tracks and posters of the 14th international
conference on World Wide Web",
title = "Retrieving multimedia {Web} objects based on
{PageRank} algorithm",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "906--907",
year = "2005",
DOI = "https://doi.org/10.1145/511446.511454",
ISBN = "1-59593-051-5",
ISBN-13 = "978-1-59593-051-4",
LCCN = "????",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Hyperlink analysis has been widely investigated to
support the retrieval of Web documents in Internet
search engines. It has been proven that the hyperlink
analysis significantly improves the relevance of the
search results and these techniques have been adopted
in many commercial search engines, e.g. Google.
However, hyperlink analysis is mostly utilized in the
ranking mechanism of Web pages only but not including
other multimedia objects, such as images and video. In
this project, we propose a modified Multimedia PageRank
algorithm to support the searching of multimedia
objects in the Web.",
acknowledgement = ack-nhfb,
keywords = "content based retrieval; HITS; hyperlink analysis;
multimedia retrieval; PageRank; web search engines",
}
@InProceedings{Yu:2005:ATD,
author = "P. S. Yu and Xin Li and Bing Liu",
title = "Adding the temporal dimension to search --- a case
study in publication search",
crossref = "Skowron:2005:PIW",
pages = "543--549",
year = "2005",
DOI = "https://doi.org/10.1109/WI.2005.21",
bibdate = "Thu May 06 16:18:03 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Zhang:2005:CBH,
author = "Junlin Zhang and Le Sun and Quan Zhou",
title = "A cue-based hub-authority approach for multi-document
text summarization",
crossref = "IEEE:2005:PII",
pages = "642--645",
year = "2005",
DOI = "https://doi.org/10.1109/NLPKE.2005.1598815",
bibdate = "Thu May 06 15:31:40 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{Zhu:2005:DPC,
author = "Yangbo Zhu and Shaozhi Ye and Xing Li",
editor = "{ACM}",
booktitle = "Conference on Information and Knowledge Management
Proceedings of the 14th ACM international conference on
Information and knowledge management",
title = "Distributed {PageRank} computation based on iterative
aggregation-disaggregation methods",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "578--585",
year = "2005",
DOI = "https://doi.org/10.1145/1099554.1099705",
ISBN = "1-59593-140-6",
ISBN-13 = "978-1-59593-140-5",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "PageRank has been widely used as a major factor in
search engine ranking systems. However, global link
graph information is required when computing PageRank,
which causes prohibitive communication cost to achieve
accurate results in distributed solution. In this
paper, we propose a distributed PageRank computation
algorithm based on iterative aggregation-disaggregation
(IAD) method with Block Jacobi smoothing. The basic
idea is divide-and-conquer. We treat each web site as a
node to explore the block structure of hyperlinks.
Local PageRank is computed by each node itself and then
updated with a low communication cost with a
coordinator. We prove the global convergence of the
Block Jacobi method and then analyze the communication
overhead and major advantages of our algorithm.
Experiments on three real web graphs show that our
method converges 5-7 times faster than the traditional
Power method. We believe our work provides an efficient
and practical distributed solution for PageRank on
large scale Web graphs.",
acknowledgement = ack-nhfb,
keywords = "block Jacobi; distributed search engines; iterative
aggregation-disaggregation; PageRank",
}
@InProceedings{Akian:2006:PMS,
author = "Marianne Akian and St{\'e}phane Gaubert and Laure
Ninove",
editor = "Christian Commault and Nicolas Marchand",
booktitle = "{Positive systems: proceedings of the second
Multidisciplinary International Symposium on Positive
Systems: Theory and Applications (POSTA 06), Grenoble,
France, Aug. 30-31, Sept. 1, 2006}",
title = "The {$T$-PageRank}: a model of self-validating effects
of web surfing",
volume = "341",
publisher = pub-SV,
address = pub-SV:adr,
pages = "239--246",
year = "2006",
DOI = "https://doi.org/10.1007/3-540-34774-7_31",
ISBN = "3-540-34774-7, 3-540-34771-2",
ISBN-13 = "978-3-540-34774-3, 978-3-540-34771-2",
LCCN = "QA402 .M86 2006",
MRclass = "68U35",
MRnumber = "MR2250261",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCIS,
ZMnumber = "1121.68007",
acknowledgement = ack-nhfb,
bookpages = "xiv + 448",
}
@InProceedings{Ali:2006:ACC,
author = "R. Ali and M. M. S. Beg",
title = "Aggregating Content and Connectivity based Techniques
for Measure of {Web} Search Quality",
crossref = "IEEE:2006:AAC",
pages = "44--49",
year = "2006",
DOI = "https://doi.org/10.1109/ADCOM.2006.4289853",
bibdate = "Thu May 06 15:35:59 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Andersen:2006:LGP,
author = "Reid Andersen and Fan Chung and Kevin Lang",
booktitle = "{FOCS '06: 47th Annual IEEE Symposium on Foundations
of Computer Science (2006)}",
title = "Local Graph Partitioning using {PageRank} Vectors",
crossref = "IEEE:2006:AIS",
pages = "475--486",
year = "2006",
DOI = "https://doi.org/10.1109/FOCS.2006.44",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4031383",
abstract = "A local graph partitioning algorithm finds a cut near
a specified starting vertex, with a running time that
depends largely on the size of the small side of the
cut, rather than the size of the input graph. In this
paper, we present a local partitioning algorithm using
a variation of PageRank with a specified starting
distribution. We derive a mixing result for PageRank
vectors similar to that for random walks, and show that
the ordering of the vertices produced by a PageRank
vector reveals a cut with small conductance. In
particular, we show that for any set C with conductance
\Phiand volume k, a PageRank vector with a certain
starting distribution can be used to produce a set with
conductance O\left( {\sqrt {\Phi \log k} } \right). We
present an improved algorithm for computing approximate
PageRank vectors, which allows us to find such a set in
time proportional to its size. In particular, we can
find a cut with conductance at most \not o , whose
small side has volume at least 2b, in time O\left( {2^b
\log ^2 m/\not o^2 } \right) where m is the number of
edges in the graph. By combining small sets found by
this local partitioning algorithm, we obtain a cut with
conductance \not o and approximately optimal balance in
time O\left( {m\log ^4 m/\not o^2 } \right).",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4031329",
}
@Article{Avrachenkov:2006:ENL,
author = "Konstantin Avrachenkov and Nelly Litvak",
title = "The effect of new links on {Google} {PageRank}",
journal = j-STOCH-MODELS,
volume = "22",
number = "2",
pages = "319--331",
year = "2006",
CODEN = "CSSME8",
DOI = "https://doi.org/10.1080/15326340600649052",
ISSN = "1532-6349",
MRclass = "68U35 (90B18 91D30)",
MRnumber = "MR2220968 (2007f:68227)",
MRreviewer = "Mirel Co{\c{s}}ulschi",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1094.68005",
acknowledgement = ack-nhfb,
fjournal = "Stochastic Models",
}
@Article{Avrachenkov:2006:PSF,
author = "Konstantin Avrachenkov and Dmitri Lebedev",
title = "{PageRank} of scale-free growing networks",
journal = j-INTERNET-MATH,
volume = "3",
number = "2",
pages = "207--231",
year = "2006",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "05C80 (68M10 68R10 68U35)",
MRnumber = "MR2321830 (2008c:05162)",
MRreviewer = "Mirel Co{\c{s}}ulschi",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1204906139",
ZMnumber = "1122.68406",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Baeza-Yates:2006:GPD,
author = "Ricardo Baeza-Yates and Paolo Boldi and Carlos
Castillo",
editor = "{ACM}",
booktitle = "Annual ACM Conference on Research and Development in
Information Retrieval Proceedings of the 29th annual
international ACM SIGIR conference on Research and
development in information retrieval",
title = "Generalizing {PageRank}: damping functions for
link-based ranking algorithms",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "308--315",
year = "2006",
DOI = "https://doi.org/10.1007/s10791-005-6993-5",
ISBN = "1-59593-369-7",
ISBN-13 = "978-1-59593-369-0",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "This paper introduces a family of link-based ranking
algorithms that propagate page importance through
links. In these algorithms there is a damping function
that decreases with distance, so a direct link implies
more endorsement than a link through a long path.
PageRank is the most widely known ranking function of
this family.The main objective of this paper is to
determine whether this family of ranking techniques has
some interest per se, and how different choices for the
damping function impact on rank quality and on
convergence speed. Even though our results suggest that
PageRank can be approximated with other simpler forms
of rankings that may be computed more efficiently, our
focus is of more speculative nature, in that it aims at
separating the kernel of PageRank, that is, link-based
importance propagation, from the way propagation decays
over paths.We focus on three damping functions, having
linear, exponential, and hyperbolic decay on the
lengths of the paths. The exponential decay corresponds
to PageRank, and the other functions are new. Our
presentation includes algorithms, analysis, comparisons
and experiments that study their behavior under
different parameters in real Web graph data.Among other
results, we show how to calculate a linear
approximation that induces a page ordering that is
almost identical to PageRank's using a fixed small
number of iterations; comparisons were performed using
Kendall's $ \tau $ on large domain datasets.",
acknowledgement = ack-nhfb,
keywords = "link analysis; link-based ranking; web graphs",
}
@InProceedings{Bansal:2006:ADC,
author = "T. Bansal and P. Ghanshani and R. C. Joshi",
title = "An Application Dependent Communication Protocol for
Wireless Sensor Networks",
crossref = "IEEE:2006:IIM",
pages = "120--120",
year = "2006",
DOI = "https://doi.org/10.1109/ICNICONSMCL.2006.46",
bibdate = "Thu May 06 16:24:09 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@Article{Bao:2006:LPD,
author = "Ying Bao and Yong Liu",
title = "Limit of {PageRank} with damping factor",
journal = j-DYN-CONTIN-DISCR-IMPULS-B,
volume = "13",
number = "3-4",
pages = "497--504",
year = "2006",
CODEN = "DCDIS4",
ISSN = "1492-8760",
MRclass = "68U35 (60J27 68P20 68W40)",
MRnumber = "MR2208501",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1100.60040",
acknowledgement = ack-nhfb,
fjournal = "Dynamics of Continuous, Discrete \& Impulsive Systems.
Series B. Applications \& Algorithms",
}
@InProceedings{Becchetti:2006:DPF,
author = "Luca Becchetti and Carlos Castillo",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceedings of
the 15th international conference on World Wide Web",
title = "The distribution of {PageRank} follows a power-law
only for particular values of the damping factor",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "941--942",
year = "2006",
DOI = "https://doi.org/10.1016/S1389-1286(00)00063-3",
ISBN = "1-59593-323-9",
ISBN-13 = "978-1-59593-323-2",
LCCN = "????",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We show that the empirical distribution of the
PageRank values in a large set of Web pages does not
follow a power-law except for some particular choices
of the damping factor. We argue that for a graph with
an in-degree distribution following a power-law with
exponent between 2.1 and 2.2, choosing a damping factor
around 0.85 for PageRank yields a power-law
distribution of its values. We suggest that power-law
distributions of PageRank in Web graphs have been
observed because the typical damping factor used in
practice is between 0.85 and 0.90.",
acknowledgement = ack-nhfb,
keywords = "pagerank distribution; web graph",
}
@Article{Berkhin:2006:BCA,
author = "Pavel Berkhin",
title = "Bookmark-coloring algorithm for personalized
{PageRank} computing",
journal = j-INTERNET-MATH,
volume = "3",
number = "1",
pages = "41--62",
year = "2006",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68U35 (68M10 68R10); 68P10 68M10 68P20 68W05",
MRnumber = "MR2283883 (2007k:68134)",
MRreviewer = "Mirel Co{\c{s}}ulschi",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1175266367",
ZMnumber = "1113.68375",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@Article{Boldi:2006:GFG,
author = "Paolo Boldi and Violetta Lonati and Massimo Santini
and Sebastiano Vigna",
title = "Graph fibrations, graph isomorphism, and {PageRank}",
journal = j-INFORM-THEOR-APPL,
volume = "40",
number = "2",
pages = "227--253",
year = "2006",
CODEN = "RSITD7, RITAE4",
DOI = "https://doi.org/10.1051/ita:2006004",
ISSN = "0988-3754 (print), 1290-385X (electronic)",
ISSN-L = "0988-3754",
MRclass = "68U35 (05C60 60J10 60J20 68R10 94C15)",
MRnumber = "MR2252637 (2007h:68204)",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1112.68002",
acknowledgement = ack-nhfb,
fjournal = "Theoretical Informatics and Applications. Informatique
Th\'eorique et Applications",
}
@Article{Bollen:2006:JS,
author = "Johan Bollen and Marko A. Rodriquez and Herbert {Van
de Sompel}",
title = "Journal status",
journal = j-SCIENTOMETRICS,
volume = "69",
number = "3",
pages = "669--687",
month = dec,
year = "2006",
CODEN = "SCNTDX",
DOI = "https://doi.org/10.1007/s11192-006-0176-z",
ISSN = "0138-9130 (print), 1588-2861 (electronic)",
ISSN-L = "0138-9130",
bibdate = "Tue Aug 11 17:28:42 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/article/10.1007/s11192-006-0176-z",
acknowledgement = ack-nhfb,
fjournal = "Scientometrics",
journal-URL = "http://link.springer.com/journal/11192",
}
@Article{Brezinski:2006:PVP,
author = "Claude Brezinski and Michela Redivo-Zaglia",
title = "The {PageRank} vector: properties, computation,
approximation, and acceleration",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "28",
number = "2",
pages = "551--575",
year = "2006",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/050626612",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
MRclass = "68U35 (65F15)",
MRnumber = "MR2255342 (2007h:68205)",
MRreviewer = "Mirel Co{\c{s}}ulschi",
bibdate = "Wed May 5 19:28:01 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1116.65042",
abstract = "An important problem in Web search is determining the
importance of each page. After introducing the main
characteristics of this problem, we will see that, from
the mathematical point of view, it could be solved by
computing the left principal eigenvector (the PageRank
vector) of a matrix related to the structure of the Web
by using the power method. We will give expressions of
the PageRank vector and study the mathematical
properties of the power method. Various Pad{\'e}-style
approximations of the PageRank vector will be given.
Since the convergence of the power method is slow, it
has to be accelerated. This problem will be discussed.
Recently, several acceleration methods were proposed.
We will give a theoretical justification for these
methods. In particular, we will generalize the recently
proposed Quadratic Extrapolation, and we interpret it
on the basis of the method of moments of Vorobyev, and
as a Krylov subspace method. Acceleration results are
given for the various epsilon -algorithms, and for the
E -algorithm. Other algorithms for this problem are
also discussed.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
}
@Article{Brinkmeier:2006:PR,
author = "Michael Brinkmeier",
title = "{PageRank} revisited",
journal = j-TOIT,
volume = "6",
number = "3",
pages = "282--301",
month = aug,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1145/1151087.1151090",
ISSN = "1533-5399 (print), 1557-6051 (electronic)",
ISSN-L = "1533-5399",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "http://www.acm.org/pubs/contents/journals/toit/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/toit.bib",
abstract = "PageRank, one part of the search engine Google, is one
of the most prominent link-based rankings of documents
in the World Wide Web. Usually it is described as a
Markov chain modeling a specific random surfer. In this
article, an alternative representation as a power
series is given. Nonetheless, it is possible to
interpret the values as probabilities in a random
surfer setting, differing from the usual one. Using the
new description we restate and extend some results
concerning the convergence of the standard iteration
used for PageRank. Furthermore we take a closer look at
sinks and sources, leading to some suggestions for
faster implementations.",
acknowledgement = ack-nhfb,
fjournal = "ACM Transactions on Internet Technology (TOIT)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J780",
keywords = "Dynamical update; link-analysis; Markov chain;
Pagerank; personalization; random surfer; ranking
algorithm; Web graph; Web page scoring; Web search;
World Wide Web",
}
@Article{Broder:2006:EPA,
author = "A. Z. Broder and R. Lempel and F. Maghoul and J.
Pedersen",
title = "Efficient {PageRank} approximation via graph
aggregation",
journal = j-INF-RETR,
volume = "9",
number = "2",
pages = "123--138",
month = mar,
year = "2006",
CODEN = "IFRTFY",
DOI = "https://doi.org/10.1145/775152.775203",
ISSN = "1386-4564 (print), 1573-7659 (electronic)",
ISSN-L = "1386-4564",
bibdate = "Sat May 8 18:33:07 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We present a framework for approximating random-walk
based probability distributions over Web pages using
graph aggregation. The basic idea is to partition the
graph into classes of quasi-equivalent vertices, to
project the page-based random walk to be approximated
onto those classes, and to compute the stationary
probability distribution of the resulting class-based
random walk. From this distribution we can quickly
reconstruct a distribution on pages. In particular, our
framework can approximate the well-known PageRank
distribution by setting the classes according to the
set of pages on each Web host. \par
We experimented on a Web-graph containing over 1.4
billion pages and over 6.6 billion links from a crawl
of the Web conducted by AltaVista in September 2003. We
were able to produce a ranking that has Spearman
rank-order correlation of 0.95 with respect to
PageRank. The clock time required by a simplistic
implementation of our method was less than half the
time required by a highly optimized implementation of
PageRank, implying that larger speedup factors are
probably possible.",
acknowledgement = ack-nhfb,
fjournal = "Information Retrieval",
keywords = "Citation and link analysis; Web IR",
}
@Article{Bryan:2006:ELA,
author = "Kurt Bryan and Tanya Leise",
title = "The \$25,000,000,000 Eigenvector: The Linear Algebra
behind {Google}",
journal = j-SIAM-REVIEW,
volume = "48",
number = "3",
pages = "569--581",
month = "????",
year = "2006",
CODEN = "SIREAD",
DOI = "https://doi.org/10.1137/050623280",
ISSN = "0036-1445 (print), 1095-7200 (electronic)",
ISSN-L = "0036-1445",
bibdate = "Tue Dec 2 17:02:29 MST 2008",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SIREV/48/3;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/siamreview.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Review",
journal-URL = "http://epubs.siam.org/sirev",
keywords = "PageRank; singular-value decomposition; SVD",
}
@InProceedings{Chongsuntornsri:2006:ATT,
author = "Aekkasit Chongsuntornsri and Ohm Sornil",
booktitle = "{ISCIT '06: International Symposium on Communications
and Information Technologies (2006)}",
title = "An Automatic {Thai} Text Summarization Using Topic
Sensitive {PageRank}",
crossref = "IEEE:2006:CIT",
pages = "547--552",
year = "2006",
DOI = "https://doi.org/10.1109/ISCIT.2006.340009",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4141445",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4141327",
}
@InProceedings{Davis:2006:EGP,
author = "Jason V. Davis and Inderjit S. Dhillon",
editor = "{ACM}",
booktitle = "International Conference on Knowledge Discovery and
Data Mining Proceedings of the 12th ACM SIGKDD
international conference on Knowledge discovery and
data mining",
title = "Estimating the global {PageRank} of {Web}
communities",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "116--125",
year = "2006",
DOI = "https://doi.org/10.1145/1099554.1099583",
ISBN = "1-59593-339-5",
ISBN-13 = "978-1-59593-339-3",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Localized search engines are small-scale systems that
index a particular community on the web. They offer
several benefits over their large-scale counterparts in
that they are relatively inexpensive to build, and can
provide more precise and complete search capability
over their relevant domains. One disadvantage such
systems have over large-scale search engines is the
lack of global PageRank values. Such information is
needed to assess the value of pages in the localized
search domain within the context of the web as a whole.
In this paper, we present well-motivated algorithms to
estimate the global PageRank values of a local domain.
The algorithms are all highly scalable in that, given a
local domain of size n, they use O(n) resources that
include computation time, bandwidth, and storage. We
test our methods across a variety of localized domains,
including site-specific domains and topic-specific
domains. We demonstrate that by crawling as few as n or
2n additional pages, our methods can give excellent
global PageRank estimates.",
acknowledgement = ack-nhfb,
keywords = "Markov chain; page rank; stochastic complementation",
}
@InProceedings{DeLong:2006:CAR,
author = "Colin DeLong and Sandeep Mane and Jaideep Srivastava",
title = "Concept-Aware Ranking: Teaching an Old Graph New
Moves",
crossref = "Clifton:2006:SIC",
pages = "80--88",
year = "2006",
DOI = "https://doi.org/10.1109/ICDMW.2006.49",
bibdate = "Thu May 06 15:42:10 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Desikan:2006:DCA,
author = "Prasanna Kumar Desikan and Nishith Pathak and Jaideep
Srivastava and Vipin Kumar",
editor = "{ACM}",
booktitle = "{Proceedings of the 6th international conference on
Web engineering}",
title = "Divide and conquer approach for efficient {PageRank}
computation",
volume = "263",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "233--240",
year = "2006",
DOI = "https://doi.org/10.1145/988672.988714",
ISBN = "1-59593-352-2",
ISBN-13 = "978-1-59593-352-2",
LCCN = "????",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "PageRank is a popular ranking metric for large graphs
such as the World Wide Web. Current research techniques
for improving computational efficiency of PageRank have
focused on improving the I/O cost, convergence and
parallelizing the computation process. In this paper,
we propose a divide and conquer strategy for efficient
computation of PageRank. The strategy is different from
contemporary improvements in that it can be combined
with any existing enhancements to PageRank, giving way
to an entire class of more efficient algorithms. We
present a novel graph-partitioning technique for
dividing the graph into subgraphs, on which computation
can be performed independently. This approach has two
significant benefits. Firstly, since the approach
focuses on work-reduction, it can be combined with any
existing enhancements to PageRank. Secondly, the
proposed approach leads naturally into developing an
incremental approach for computation of such ranking
metrics given that these large graphs evolve over a
period of time. The partitioning technique is both
lossless and independent of the type (variant) of
PageRank computation algorithm used. The experimental
results for a static single graph (graph at a single
time instance) as well as for the incremental
computation in case of evolving graphs, illustrate the
utility of our novel partitioning approach. The
proposed approach can also be applied for the
computation of any other metric based on first order
Markov chain model.",
acknowledgement = ack-nhfb,
keywords = "efficient computation; graph partitioning; PageRank;
ranking measures",
}
@Article{Gleich:2006:APP,
author = "David Gleich and Marzia Polito",
title = "Approximating personalized {PageRank} with minimal use
of web graph data",
journal = j-INTERNET-MATH,
volume = "3",
number = "3",
pages = "257--294",
year = "2006",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68U35 (05C85 05C90 68M10 68R10)",
MRnumber = "MR2372544 (2008m:68217)",
MRreviewer = "Mirel Co{\c{s}}ulschi",
bibdate = "Wed May 5 19:28:02 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1204906158",
ZMnumber = "1147.68350",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@Article{Golub:2006:ATA,
author = "G. H. Golub and C. Greif",
title = "An {Arnoldi}-type algorithm for computing {PageRank}",
journal = j-BIT,
volume = "46",
number = "4",
pages = "759--771",
year = "2006",
CODEN = "BITTEL, NBITAB",
DOI = "https://doi.org/10.1007/s10543-006-0091-y",
ISSN = "0006-3835 (print), 1572-9125 (electronic)",
ISSN-L = "0006-3835",
MRclass = "65F15",
MRnumber = "MR2285207 (2008a:65073)",
MRreviewer = "Jan Mandel",
bibdate = "Wed May 5 19:28:02 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "BIT. Numerical Mathematics",
journal-URL = "http://link.springer.com/journal/10543",
}
@InProceedings{Hamid:2006:RDU,
author = "Noorisyam Hamid and Fazilah Haron and Chan Huah Yong",
title = "Resource Discovery Using {PageRank} Technique in Grid
Environment",
crossref = "Turner:2006:SII",
volume = "1",
pages = "135--140",
year = "2006",
DOI = "https://doi.org/10.1109/CCGRID.2006.87",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1630807",
abstract = "The grid deals with large scale and ever-expanding
environment which contains million of users and
resources. For this reason, resource selection has been
a challenging task especially in meeting user's demand
for a quality of service (QoS). A quality of service is
the ability to serve a job by providing quality and
reliable resource in fulfilling the user's need.
Quality and reliable resource selections naturally
yield excellent and quality results. The background of
the users and where the resource belongs to are
important in determining the quality of a resource.
This paper concerns with efficient and quality-based
resource discovery using Condor ClassAd and PageRank
technique in order to achieve a quality resource
matching. The paper discusses how quality of users and
resources are determined and considered in the
discovery process prior to allocating jobs to
resources.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10856",
}
@InProceedings{Huang:2006:TPA,
author = "Decai Huang and Huachun Qi and Yuan Yuan and Yue-feng
Zheng",
booktitle = "{WCICA 2006: The Sixth World Congress on Intelligent
Control and Automation}",
title = "{TC-PageRank} Algorithm Based on Topic Correlation",
crossref = "IEEE:2006:WSW",
volume = "2",
pages = "5943--5946",
year = "2006",
DOI = "https://doi.org/10.1109/WCICA.2006.1714219",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1714219",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11210",
}
@Article{Ipsen:2006:CAP,
author = "Ilse C. F. Ipsen and Steve Kirkland",
title = "Convergence analysis of a {PageRank} updating
algorithm by {Langville} and {Meyer}",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "27",
number = "4",
pages = "952--967",
year = "2006",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/S0895479804439808",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
MRclass = "65F30 (15A51 68U35); 65F15 65F10 15A18 15A42 65C40
15A51 68P10 60J22",
MRnumber = "MR2205606 (2006i:65070)",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1108.65030",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
}
@Article{Ipsen:2006:MPA,
author = "Ilse C. F. Ipsen and Rebecca S. Wills",
title = "Mathematical properties and analysis of {Google}'s
{PageRank}",
journal = "Bol. Soc. Esp. Mat. Apl. S$\vec{\rm e}$MA",
volume = "34",
pages = "191--196",
year = "2006",
CODEN = "????",
ISSN = "1575-9822",
MRclass = "65F15 (15A51)",
MRnumber = "MR2296216",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Bolet\'\i n de la Sociedad Espa\~nola de Matem\'atica
Aplicada. S$\vec{\rm e}$MA",
}
@InProceedings{Kabutoya:2006:QEL,
author = "Yutaka Kabutoya and Takayuki Yumoto and Satoshi Oyama
and Keishi Tajima and Katsumi Tanaka",
booktitle = "{Proceedings of the 22nd International Conference on
Data Engineering Workshops (2006)}",
title = "Quality Estimation of Local Contents Based on
{PageRank} Values of {Web} Pages",
crossref = "Barga:2006:IPI",
pages = "x134--x134",
year = "2006",
DOI = "https://doi.org/10.1109/ICDEW.2006.121",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1623929",
abstract = "Recently, it is getting more frequent to search not
Web contents but local contents, e.g., by Google
Desktop Search. Google succeeded in the Web search
because of its PageRank algorithm for the ranking of
the search results. PageRank estimates the quality of
Web pages based on their popularity, which in turn is
estimated by the number and the quality of pages
referring to them through hyperlinks. This algorithm,
however, is not applicable when we search local
contents without link structure, such as text data. In
this research, we propose a method to estimate the
quality of local contents without link structure by
using the PageRank values of Web contents similar to
them. Based on this estimation, we can rank the desktop
search results. Furthermore, this method enables us to
search contents across different resources such as Web
contents and local contents. In this paper, we applied
this method to Web contents, calculated the scores that
estimate their quality, and we compare them with their
page quality scores by PageRank.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10810",
}
@Article{Kirkland:2006:CES,
author = "S. Kirkland",
title = "Conditioning of the entries in the stationary vector
of a {Google}-type matrix",
journal = j-LINEAR-ALGEBRA-APPL,
volume = "418",
number = "2--3",
pages = "665--681",
day = "15",
month = oct,
year = "2006",
CODEN = "LAAPAW",
DOI = "https://doi.org/10.1016/j.laa.2006.03.007",
ISSN = "0024-3795 (print), 1873-1856 (electronic)",
ISSN-L = "0024-3795",
bibdate = "Wed Mar 30 14:18:57 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/00243795",
acknowledgement = ack-nhfb,
fjournal = "Linear Algebra and its Applications",
journal-URL = "http://www.sciencedirect.com/science/journal/00243795",
keywords = "condition number; PageRank; stationary vector;
stochastic matrix",
}
@InProceedings{Kozakiewicz:2006:TLA,
author = "A. Kozakiewicz and A. Karbowskr",
title = "A Two-Level Approach to Building a Campus Grid",
crossref = "IEEE:2006:ISP",
pages = "121--126",
year = "2006",
DOI = "https://doi.org/10.1109/PARELEC.2006.11",
bibdate = "Thu May 06 15:58:28 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Book{Langville:2006:GPB,
author = "Amy N. Langville and Carl D. (Carl Dean) Meyer",
title = "{Google}'s {PageRank} and beyond: the science of
search engine rankings",
publisher = pub-PRINCETON,
address = pub-PRINCETON:adr,
pages = "x + 224",
year = "2006",
ISBN = "0-691-12202-4 (hardcover)",
ISBN-13 = "978-0-691-12202-1 (hardcover)",
LCCN = "TK5105.885.G66 L36 2006",
MRclass = "68-02 (00-01 00A05 15A18 68U35)",
MRnumber = "MR2262054 (2007h:68002)",
MRreviewer = "Jiu Ding",
bibdate = "Fri Oct 23 16:04:57 MDT 2009",
bibsource = "https://www.math.utah.edu/pub/tex/bib/master.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
URL = "http://www.loc.gov/catdir/enhancements/fy0654/2005938841-b.html;
http://www.loc.gov/catdir/enhancements/fy0654/2005938841-d.html;
http://www.loc.gov/catdir/enhancements/fy0668/2005938841-t.html",
ZMnumber = "1104.68042",
acknowledgement = ack-nhfb,
libnote = "Not in my library.",
subject = "Google; Web search engines; Web sites; Ratings;
Mathematics; Internet searching; World Wide Web;
Subject access",
tableofcontents = "1: Introduction to Web Search Engines \\
2: Crawling, Indexing, and Query Processing \\
3: Ranking Webpages by Popularity \\
4: The Mathematics of Google's PageRank \\
5: Parameters in the PageRank Model \\
6: The Sensitivity of PageRank \\
7: The PageRank Problem as a Linear System \\
8: Issues in Large-Scale Implementation of PageRank \\
9: Accelerating the Computation of PageRank \\
10: Updating the PageRank Vector \\
11: The HITS Method for Ranking Webpages \\
12: Other Link Methods for Ranking Webpages \\
13: The Future of Web Information Retrieval \\
14: Resources for Web Information Retrieval \\
15: The Mathematics Guide",
}
@Article{Langville:2006:UMC,
author = "Amy N. Langville and Carl D. Meyer",
title = "Updating {Markov} chains with an eye on {Google}'s
{PageRank}",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "27",
number = "4",
pages = "968--987",
year = "2006",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/040619028",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
MRclass = "60J10 (65C40 68P20 68U35); 60J10 65C40 15A51 65F10
65F15 65F30 65F50 68P20 68P10 15A99 15-04 15A18 15A06",
MRnumber = "MR2205607",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1098.60073",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
}
@InProceedings{Lin:2006:PNL,
author = "Zhenjiang Lin and I. King and M. R. Lyu",
title = "{PageSim}: a Novel Link-Based Similarity Measure for
the {World Wide Web}",
crossref = "Nishida:2006:IWA",
pages = "687--693",
year = "2006",
DOI = "https://doi.org/10.1109/WI.2006.127",
bibdate = "Thu May 06 16:01:07 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InCollection{MadriddelaVega:2006:NLA,
author = "Humberto {Madrid de la Vega} and Valia Guerra Ones and
Marisol Flores Garrido",
booktitle = "{Papers of the Mexican Mathematical Society
(Spanish)}",
title = "The numerical linear algebra of {Google}'s
{PageRank}",
volume = "36",
publisher = "Soc. Mat. Mexicana",
address = "M\'exico",
pages = "33--52",
year = "2006",
ISBN = "????",
ISBN-13 = "????",
MRclass = "65F15; 68P10 68M10 65F10 65F15 65F30 65F50",
MRnumber = "MR2347016 (2008j:65059)",
MRreviewer = "Juan R. Torregrosa",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Aportaciones Mat. Comun.",
ZMnumber = "1119.68357",
acknowledgement = ack-nhfb,
}
@InProceedings{Murata:2006:EKW,
author = "T. Murata and K. Saito",
title = "Extracting Keywords of {Web} Users' Interests and
Visualizing their Routine Visits",
crossref = "IEEE:2006:ICC",
pages = "1--66",
year = "2006",
DOI = "https://doi.org/10.1109/ICARCV.2006.345367",
bibdate = "Thu May 06 15:04:16 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{Neate:2006:CNF,
author = "B. Neate and W. Irwin and N. Churcher",
title = "{CodeRank}: a new family of software metrics",
crossref = "IEEE:2006:ASE",
pages = "369--378 (check??)",
year = "2006",
bibdate = "Thu May 06 15:14:28 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Ono:2006:IWS,
author = "H. Ono and M. Toyoda and M. Kitsuregawa",
title = "Identifying {Web} Spam by Densely Connected Sites and
its Statistics in a {Japanese Web} Snapshot",
crossref = "Barga:2006:IPI",
pages = "x131--x131",
year = "2006",
DOI = "https://doi.org/10.1109/ICDEW.2006.64",
bibdate = "Thu May 06 17:00:09 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Article{Pandurangan:2006:UPC,
author = "Gopal Pandurangan and Prabhakar Raghavan and Eli
Upfal",
title = "Using {PageRank} to characterize web structure",
journal = j-INTERNET-MATH,
volume = "3",
number = "1",
pages = "1--20",
year = "2006",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68U35 (05C07 05C80 68M10); 68M10 68P10 68W05",
MRnumber = "MR2283881 (2007k:68135)",
MRreviewer = "Mirel Co{\c{s}}ulschi",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1175266365",
ZMnumber = "1113.68313",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Parreira:2006:EDP,
author = "Josiane Xavier Parreira and Debora Donato and
Sebastian Michel and Gerhard Weikum",
editor = "Umeshwar Dayal and others",
booktitle = "Proceedings of the 32nd International Conference on
Very Large Data Bases",
title = "Efficient and decentralized {PageRank} approximation
in a peer-to-peer {Web} search network",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "415--426",
year = "2006",
DOI = "https://doi.org/10.1109/ICDCS.2005.84",
ISBN = "1-59593-385-9",
ISBN-13 = "978-1-59593-385-0",
LCCN = "QA76.9.D3 I61 2006",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "PageRank-style (PR) link analyses are a cornerstone of
Web search engines and Web mining, but they are
computationally expensive. Recently, various techniques
have been proposed for speeding up these analyses by
distributing the link graph among multiple sites.
However, none of these advanced methods is suitable for
a fully decentralized PR computation in a peer-to-peer
(P2P) network with autonomous peers, where each peer
can independently crawl Web fragments according to the
user's thematic interests. In such a setting the graph
fragments that different peers have locally available
or know about may arbitrarily overlap among peers,
creating additional complexity for the PR
computation.This paper presents the JXP algorithm for
dynamically and collaboratively computing PR scores of
Web pages that are arbitrarily distributed in a P2P
network. The algorithm runs at every peer, and it works
by combining locally computed PR scores with random
meetings among the peers in the network. It is scalable
as the number of peers on the network grows, and
experiments as well as theoretical arguments show that
JXP scores converge to the true PR scores that one
would obtain by a centralized computation.",
acknowledgement = ack-nhfb,
bookpages = "xxxi + 1269 (two volumes)",
}
@InProceedings{Peng:2006:RWS,
author = "Wen-Chih Peng and Yu-Chin Lin",
title = "Ranking {Web} Search Results from Personalized
Perspective",
crossref = "Wombacher:2006:JCC",
pages = "12--12",
year = "2006",
DOI = "https://doi.org/10.1109/CEC-EEE.2006.72",
bibdate = "Thu May 06 15:52:27 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{Quesada:2006:HIP,
author = "A. Arratia Quesada and C. Mariju{\'a}n",
booktitle = "{Fifth Conference on Discrete Mathematics and Computer
Science (Spanish)}",
title = "How to improve the {PageRank} of a tree",
volume = "23",
publisher = "Universidad Valladolid",
address = "Secr. Publ. Intercamb. Ed., Valladolid, Spain",
pages = "71--78",
year = "2006",
ISBN = "????",
ISBN-13 = "????",
MRclass = "05C80 (68U35)",
MRnumber = "MR2325945",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Ciencias (Valladolid)",
ZMnumber = "05555980",
acknowledgement = ack-nhfb,
}
@InProceedings{Radev:2006:GBM,
author = "D. R. Radev",
title = "Graph-Based Methods for Language Processing and
Information Retrieval",
crossref = "IEEE:2006:ISL",
pages = "4--4",
year = "2006",
DOI = "https://doi.org/10.1109/SLT.2006.326781",
bibdate = "Thu May 06 16:42:55 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{Richardson:2006:BPM,
author = "Matthew Richardson and Amit Prakash and Eric Brill",
editor = "{ACM}",
booktitle = "{International World Wide Web Conference Proceedings
of the 15th international conference on World Wide
Web}",
title = "Beyond {PageRank}: machine learning for static
ranking",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "707--715",
year = "2006",
DOI = "https://doi.org/10.1145/858476.858479",
ISBN = "1-59593-323-9",
ISBN-13 = "978-1-59593-323-2",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Since the publication of Brin and Page's paper on
PageRank, many in the Web community have depended on
PageRank for the static (query-independent) ordering of
Web pages. We show that we can significantly outperform
PageRank using features that are independent of the
link structure of the Web. We gain a further boost in
accuracy by using data on the frequency at which users
visit Web pages. We use RankNet, a ranking machine
learning algorithm, to combine these and other static
features based on anchor text and domain
characteristics. The resulting model achieves a static
ranking pairwise accuracy of 67.3\% (vs. 56.7\% for
PageRank or 50\% for random).",
acknowledgement = ack-nhfb,
keywords = "PageRank; RankNet; relevance; search engines; static
ranking",
}
@InProceedings{Rungsawang:2006:PAT,
author = "Arnon Rungsawang and Bundit Manaskasemsak",
booktitle = "{PDP 2006: 14th Euromicro International Conference on
Parallel, Distributed, and Network-Based Processing}",
title = "Parallel adaptive technique for computing {PageRank}",
crossref = "IEEE:2005:EIC",
pages = "15--50",
year = "2006",
DOI = "https://doi.org/10.1109/PDP.2006.55",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1613249",
abstract = "Re-ranking the search results using PageRank is a
well-known technique used in modern search engines.
Running an iterative algorithm like PageRank on a large
web graph consumes both much computing resource and
time. This paper therefore proposes a parallel adaptive
technique for computing PageRank using the PC cluster.
Following the study of the Stanford WebBase group on
convergence patterns of PageRank scores of pages using
the conventional PageRank algorithm, PageRank scores of
most pages converge more quickly than the remainder, we
then devise our parallel adaptive algorithm to
reiterate the computation for pages whose PageRank
scores are still not converged. From experiments using
a synthesized web graph of 28 million pages and around
227 million hyperlinks, we obtain the acceleration rate
up to 6-8 times using 32 PC processors.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10741",
pagecount = "6",
}
@InProceedings{Sarlos:2006:RRS,
author = "Tam{\'a}s Sarl{\'o}s and Adr{\'a}s A. Bencz{\'u}r and
K{\'a}roly Csalog{\'a}ny and D{\'a}niel Fogaras and
Bal{\'a}zs R{\'a}cz",
editor = "ACM",
booktitle = "{Proceedings of the 15th international conference on
World Wide Web, Edinburgh, Scotland}",
title = "To randomize or not to randomize: space optimal
summaries for hyperlink analysis",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "297--306",
year = "2006",
DOI = "https://doi.org/10.1145/1135777.1135823",
ISBN = "1-59593-323-9",
ISBN-13 = "978-1-59593-323-2",
LCCN = "????",
bibdate = "Mon May 10 13:56:03 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Personalized PageRank expresses link-based page
quality around user selected pages. The only previous
personalized PageRank algorithm that can serve on-line
queries for an unrestricted choice of pages on large
graphs is our Monte Carlo algorithm [WAW 2004]. In this
paper we achieve unrestricted personalization by
combining rounding and randomized sketching techniques
in the dynamic programming algorithm of Jeh and Widom
[WWW 2003]. We evaluate the precision of approximation
experimentally on large scale real-world data and find
significant improvement over previous results. As a key
theoretical contribution we show that our algorithms
use an optimal amount of space by also improving
earlier asymptotic worst-case lower bounds. Our lower
bounds and algorithms apply to the SimRank as well; of
independent interest is the reduction of the SimRank
computation to personalized PageRank.",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@Article{Sun:2006:NPA,
author = "Huan Sun and Yimin Wei",
title = "A note on the {PageRank} algorithm",
journal = j-APPL-MATH-COMP,
volume = "179",
number = "2",
pages = "799--806",
day = "15",
month = aug,
year = "2006",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2005.11.120",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
MRclass = "65F15",
MRnumber = "MR2293192",
bibdate = "Sat Jul 12 09:02:57 MDT 2008",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/00963003",
URL = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2005.bib",
ZMnumber = "1103.68973",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003",
}
@InProceedings{Tong:2006:FRW,
author = "Hanghang Tong and C. Faloutsos and J.-Y. Pan",
title = "Fast Random Walk with Restart and Its Applications",
crossref = "Clifton:2006:SIC",
pages = "613--622",
year = "2006",
DOI = "https://doi.org/10.1109/ICDM.2006.70",
bibdate = "Thu May 06 16:55:53 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
xxcrossref = "Perner:2006:ADM",
}
@Article{Wills:2006:GPM,
author = "Rebecca S. Wills",
title = "{Google}'s {PageRank}: the math behind the search
engine",
journal = j-MATH-INTEL,
volume = "28",
number = "4",
pages = "6--11",
year = "2006",
CODEN = "MAINDC",
DOI = "https://doi.org/10.1007/BF02984696",
ISSN = "0343-6993 (print), 1866-7414 (electronic)",
ISSN-L = "0343-6993",
MRclass = "05C80 (00A99 15A18)",
MRnumber = "MR2272767",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "The Mathematical Intelligencer",
}
@InProceedings{Wissner-Gross:2006:PTR,
author = "A. D. Wissner-Gross",
title = "Preparation of Topical Reading Lists from the Link
Structure of {Wikipedia}",
crossref = "IEEE:2006:SIC",
pages = "825--829",
year = "2006",
DOI = "https://doi.org/10.1109/ICALT.2006.1652568",
bibdate = "Thu May 06 16:05:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Yang:2006:PRG,
author = "Haixuan Yang and Irwin King and M. R. Lyu",
title = "Predictive Random Graph Ranking on the {Web}",
crossref = "IEEE:2006:IJC",
pages = "1825--1832",
year = "2006",
DOI = "https://doi.org/10.1109/IJCNN.2006.246901",
bibdate = "Thu May 06 16:06:14 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Zhang:2006:XLM,
author = "Yi Zhang and Lei Zhang and Yan Zhang and Xiaoming Li",
title = "{XRank}: Learning More from {Web} User Behaviors",
crossref = "Jeong:2006:SII",
pages = "36--36",
year = "2006",
DOI = "https://doi.org/10.1109/CIT.2006.198",
bibdate = "Thu May 06 16:14:21 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Zhuang:2006:ACM,
author = "Yueting Zhuang and Hanhuai Shan and Fei Wu",
booktitle = "{Proceedings of the 2006 12th International
Multi-Media Modelling Conference}",
title = "An approach for cross-media retrieval with
cross-reference graph and {PageRank}",
crossref = "Feng:2006:IMM",
pages = "??--??",
year = "2006",
DOI = "https://doi.org/10.1109/MMMC.2006.1651316",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1651316",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10988",
pagecount = "8",
}
@InProceedings{Al-Saffar:2007:EBU,
author = "Sinan Al-Saffar and Gregory Heileman",
editor = "{IEEE}",
booktitle = "{IEEE\slash WIC\slash ACM International Conference on
Web Intelligence}",
title = "Experimental Bounds on the Usefulness of Personalized
and Topic-Sensitive {PageRank}",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "671--675",
year = "2007",
DOI = "https://doi.org/10.1109/WI.2007.75",
ISBN = "0-7695-3026-5",
ISBN-13 = "978-0-7695-3026-0",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4427171",
abstract = "PageRank is an algorithm used by several search
engines to rank web documents according to their
assumed relevance and popularity deduced from the Web's
link structure. PageRank determines a global ordering
of candidate search results according to each page's
popularity as determined by the number and importance
of pages linking to these results. Personalized and
topic-sensitive PageRank are variants of the algorithm
that return a local ranking based on each user's
preferences as biased by a set of pages they trust or
topics they prefer. In this paper we compare
personalized and topic-sensitive local PageRanks to the
global PageRank showing experimentally how similar or
dissimilar results of personalization can be to the
original global rank results and to other
personalizations. Our approach is to examine a snapshot
of the Web and determine how advantageous
personalization can be in the best and worst cases and
how it performs at various values of the damping factor
in the PageRank formula.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043",
}
@InProceedings{Al-Saffar:2007:PTS,
author = "S. Al-Saffar and G. Heileman",
title = "Personalized and Topic-Sensitive {PageRank}",
crossref = "Lin:2007:PIW",
pages = "671--675",
year = "2007",
DOI = "https://doi.org/10.1109/WI.2007.75",
bibdate = "Fri Feb 19 15:48:36 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Andersen:2007:DSD,
author = "Reid Andersen and Fan Chung",
title = "Detecting sharp drops in {PageRank} and a simplified
local partitioning algorithm",
crossref = "Cai:2007:TAM",
pages = "1--12",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-72504-6_1",
MRclass = "68M10 (68U35)",
MRnumber = "MR2374293 (2008m:68006)",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "05211353",
abstract = "We show that whenever there is a sharp drop in the
numerical rank defined by a personalized PageRank
vector, the location of the drop reveals a cut with
small conductance. We then show that for any cut in the
graph, and for many starting vertices within that cut,
an approximate personalized PageRank vector will have a
sharp drop sufficient to produce a cut with conductance
nearly as small as the original cut. Using this
technique, we produce a nearly linear time local
partitioning algorithm whose analysis is simpler than
previous algorithms.",
acknowledgement = ack-nhfb,
}
@InProceedings{Andersen:2007:LCP,
author = "Reid Andersen and Christian Borgs and Jennifer Chayes
and John Hopcraft and Vahab S. Mirrokni and Shang-Hua
Teng",
title = "Local computation of {PageRank} contributions",
crossref = "Bonato:2007:AMW",
pages = "150--165",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-77004-6_12",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
MRclass = "05C90 (68R10 68U35 68W25)",
MRnumber = "MR2504913 (2010f:05175)",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
acknowledgement = ack-nhfb,
}
@InProceedings{Andersen:2007:LPD,
author = "Reid Andersen and Fan Chung and Kevin Lang",
title = "Local partitioning for directed graphs using
{PageRank}",
crossref = "Bonato:2007:AMW",
pages = "166--178",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-77004-6_13",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
MRclass = "05C20 (68M10 68R10 68U35)",
MRnumber = "MR2504914 (2010f:05082)",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
acknowledgement = ack-nhfb,
}
@Article{Andersen:2007:UPL,
author = "Reid Andersen and Fan Chung and Kevin Lang",
title = "Using {PageRank} to locally partition a graph",
journal = j-INTERNET-MATH,
volume = "4",
number = "1",
pages = "35--64",
year = "2007",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "05C70 (05C50 05C85 05C90 68R10)",
MRnumber = "MR2492174 (2009k:05142)",
MRreviewer = "Anthony Bonato",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1243430567",
ZMnumber = "1170.68302",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Avrachenkov:2007:DPM,
author = "Konstantin Avrachenkov and Nelly Litvak and Kim Son
Pham",
title = "Distribution of {PageRank} mass among principle
components of the web",
crossref = "Bonato:2007:AMW",
pages = "16--28",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-77004-6_2",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
MRclass = "68U35 (15A18)",
MRnumber = "MR2504904",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
ZMnumber = "1136.68319",
acknowledgement = ack-nhfb,
}
@Article{Avrachenkov:2007:MCM,
author = "K. Avrachenkov and N. Litvak and D. Nemirovsky and N.
Osipova",
title = "{Monte Carlo} Methods in {PageRank} Computation: When
One Iteration is Sufficient",
journal = j-SIAM-J-NUMER-ANAL,
volume = "45",
number = "2",
pages = "890--904",
month = feb,
year = "2007",
CODEN = "SJNAAM",
DOI = "https://doi.org/10.1137/050643799",
ISSN = "0036-1429 (print), 1095-7170 (electronic)",
ISSN-L = "0036-1429",
MRclass = "60J20 (60J10 65C05); 60J20 65C05 60J05 60J10 65C40",
MRnumber = "MR2300301",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "http://siamdl.aip.org/dbt/dbt.jsp?KEY=SJNAAM&Volume=45&Issue=2;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/siamjnumeranal2000.bib",
ZMnumber = "1146.60056",
abstract = "PageRank is one of the principle criteria according to
which Google ranks Web pages. PageRank can be
interpreted as a frequency of visiting a Web page by a
random surfer, and thus it reflects the popularity of a
Web page. Google computes the PageRank using the power
iteration method, which requires about one week of
intensive computations. In the present work we propose
and analyze Monte Carlo-type methods for the PageRank
computation. There are several advantages of the
probabilistic Monte Carlo methods over the
deterministic power iteration method: Monte Carlo
methods already provide good estimation of the PageRank
for relatively important pages after one iteration;
Monte Carlo methods have natural parallel
implementation; and finally, Monte Carlo methods allow
one to perform continuous update of the PageRank as the
structure of the Web changes.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Numerical Analysis",
journal-URL = "http://epubs.siam.org/sinum",
keywords = "absorbing Markov chains; Google; Monte Carlo methods;
PageRank",
}
@Article{Bergstrom:2007:EMV,
author = "C. Bergstrom",
title = "Eigenfactor: Measuring the value and prestige of
scholarly journals",
journal = "College \& Research Libraries News",
volume = "68",
number = "??",
pages = "5--??",
month = "????",
year = "2007",
bibdate = "Fri Mar 11 16:15:59 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank algorithm",
}
@InProceedings{Bickson:2007:PPR,
author = "D. Bickson and D. Malkhi and Lidong Zhou",
title = "Peer-to-Peer Rating",
crossref = "Hauswirth:2007:SII",
pages = "211--218",
year = "2007",
DOI = "https://doi.org/10.1109/P2P.2007.36",
bibdate = "Thu May 06 16:58:10 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{Bidoki:2007:FFI,
author = "A. M. Z. Bidoki and N. Yazdani and P. Ghodsnia",
title = "{FICA}: a Fast Intelligent Crawling Algorithm",
crossref = "Lin:2007:PIW",
pages = "635--641",
year = "2007",
DOI = "https://doi.org/10.1109/WI.2007.91",
bibdate = "Thu May 06 16:57:29 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InBook{Boldi:2007:DIP,
author = "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
title = "A deeper investigation of {PageRank} as a function of
the damping factor",
volume = "07071",
publisher = "International Begegnungs- und Forschungszentrum
f{\"u}r Informatik",
address = "Wadern, Germany",
pages = "????",
year = "2007",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Feb 19 15:32:30 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Dagstuhl seminar proceedings",
URL = "http://drops.dagstuhl.de/opus/volltexte/2007/1072/pdf/07071.VignaSebastiano.Paper.1072",
acknowledgement = ack-nhfb,
}
@InProceedings{Boldi:2007:TPT,
author = "Paolo Boldi and Roberto Posenato and Massimo Santini
and Sebastiano Vigna",
editor = "David Hutchison and William Aiello and Andrei Broder
and Jeannette Janssen and Takeo Kanade and Josef
Kittler and Jon M. Kleinberg and Friedemann Mattern and
Evangelos Milios and John C. Mitchell and Moni Naor and
Oscar Nierstrasz and C. {Pandu Rangan} and Bernhard
Steffen and Madhu Sudan and Demetri Terzopoulos and
Doug Tygar and Moshe Y. Vardi and Gerhard Weikum",
booktitle = "{Algorithms and Models for the Web-Graph \$h
[Elektronische Ressource]: Fourth International
Workshop, WAW 2006, Banff, Canada, November
30--December 1, 2006. Revised Papers}",
title = "Traps and pitfalls of topic-biased {PageRank}",
volume = "4936",
publisher = pub-SV,
address = pub-SV:adr,
pages = "107--116",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-78808-9_10",
ISBN = "3-540-78808-5",
ISBN-13 = "978-3-540-78808-9",
bibdate = "Tue Aug 11 18:00:34 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
URL = "http://link.springer.com/chapter/10.1007/978-3-540-78808-9_10",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1007/978-3-540-78808-9",
bookpages = "x + 165",
tableofcontents = "Modelling and Mining of Networked Information
Spaces \\
Workshop on Algorithms and Models for the Web Graph \\
Expansion and Lack Thereof in Randomly Perturbed Graphs
\\
Web Structure in 2005 \\
Local/Global Phenomena in Geometrically Generated
Graphs \\
Approximating PageRank from In-Degree \\
Probabilistic Relation between In-Degree and PageRank
\\
Communities in Large Networks: Identification and
Ranking \\
Combating Spamdexing: Incorporating Heuristics in
Link-Based Ranking \\
Traps and Pitfalls of Topic-Biased PageRank \\
A Scalable Multilevel Algorithm for Graph Clustering
and Community Structure Detection \\
A Phrase Recommendation Algorithm Based on Query Stream
Mining in Web Search Engines \\
Characterization of Graphs Using Degree Cores \\
Web Structure Mining by Isolated Stars \\
Representing and Quantifying Rank \\
Change for the Web Graph",
}
@InCollection{Brezinski:2007:EMP,
author = "Claude Brezinski and Michela Redivo-Zaglia",
editor = "Andreas Frommer and Michael W. Mahoney and Daniel B.
Szyld",
booktitle = "{Web} Information Retrieval and Linear Algebra
Algorithms",
title = "Extrapolation and minimization procedures for the
{PageRank} vector",
volume = "07071",
publisher = "International Begegnungs- und Forschungszentrum
f{\"u}r Informatik",
address = "Wadern, Germany",
pages = "1862--????",
year = "2007",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Feb 19 15:32:30 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Dagstuhl seminar proceedings",
URL = "http://drops.dagstuhl.de/opus/volltexte/2007/1068/pdf/07071.RedivoZagliaMichela.Paper.1068",
acknowledgement = ack-nhfb,
}
@InProceedings{Caverlee:2007:SRW,
author = "J. Caverlee and S. Webb and L. Liu",
title = "Spam-Resilient {Web} Rankings via Influence
Throttling",
crossref = "IEEE:2007:ICI",
pages = "1--10",
year = "2007",
DOI = "https://doi.org/10.1109/IPDPS.2007.370233",
bibdate = "Thu May 06 15:12:50 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Chakrabarti:2007:DPP,
author = "Soumen Chakrabarti",
editor = "{ACM}",
booktitle = "Proceedings of the 16th international conference on
World Wide Web",
title = "Dynamic personalized {PageRank} in entity-relation
graphs",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "571--580",
year = "2007",
DOI = "https://doi.org/10.1016/S0306-4573(96)85003-5",
ISBN = "1-59593-654-8",
ISBN-13 = "978-1-59593-654-7",
LCCN = "????",
bibdate = "Sat May 8 18:33:07 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Extractors and taggers turn unstructured text into
entity-relation(ER) graphs where nodes are entities
(email, paper, person,conference, company) and edges
are relations (wrote, cited,works-for). Typed proximity
search of the form {\bf type=person NEAR company~'IBM',
paper~'XML'} is an increasingly useful search paradigm
in ER graphs. Proximity search implementations either
perform a Pagerank-like computation at query time,
which is slow, or precompute, store and combine
per-word Pageranks, which can be very expensive in
terms of preprocessing time and space. We present
HubRank, a new system for fast, dynamic,
space-efficient proximity searches in ER graphs. During
preprocessing, HubRank computes and indexes certain
'sketchy' random walk fingerprints for a small fraction
of nodes, carefully chosen using query log statistics.
At query time, a small 'active' subgraph is identified,
bordered by nodes with indexed fingerprints. These
fingerprints are adaptively loaded to various
resolutions to form approximate personalized Pagerank
vectors (PPVs). PPVs at remaining active nodes are now
computed iteratively. We report on experiments with
CiteSeer's ER graph and millions of real Cite Seer
queries. Some representative numbers follow. On our
testbed, HubRank preprocesses and indexes 52 times
faster than whole-vocabulary PPV computation. A text
index occupies 56 MB. Whole-vocabulary PPVs would
consume 102GB. If PPVs are truncated to 56 MB,
precision compared to true Pagerank drops to 0.55; in
contrast, HubRank has precision 0.91 at 63MB. HubRank's
average query time is 200-300 milliseconds; query-time
Pagerank computation takes 11 seconds on average.",
acknowledgement = ack-nhfb,
keywords = "graph proximity search; personalized pagerank",
}
@Article{Chau:2007:IWA,
author = "M. Chau and H. Chen",
title = "Incorporating {Web} Analysis Into Neural Networks: An
Example in {Hopfield} Net Searching",
journal = "IEEE Transactions on Systems, Man, and Cybernetics,
Part C: Applications and Reviews",
volume = "37",
number = "3",
pages = "352--358",
month = may,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1109/TSMCC.2007.893277",
ISSN = "1094-6977",
bibdate = "Thu May 06 16:34:52 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@Article{Chen:2007:FSG,
author = "P. Chen and H. Xie and S. Maslov and S. Redner",
title = "Finding scientific gems with {Google}'s {PageRank}
algorithm",
journal = j-J-INFORMETRICS,
volume = "1",
number = "1",
pages = "8--15",
month = jan,
year = "2007",
DOI = "https://doi.org/10.1016/j.joi.2006.06.001",
ISSN = "1751-1577 (print), 1875-5879 (electronic)",
ISSN-L = "1751-1577",
bibdate = "Tue Aug 11 16:19:16 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S1751157706000034",
acknowledgement = ack-nhfb,
fjournal = "Journal of Informetrics",
journal-URL = "http://www.sciencedirect.com/science/journal/17511577",
}
@Article{Chung:2007:HKP,
author = "Fan Chung",
title = "The heat kernel as the pagerank of a graph",
journal = j-PROC-NATL-ACAD-SCI-USA,
volume = "104",
number = "50",
pages = "19735--19740",
day = "11",
month = dec,
year = "2007",
CODEN = "PNASA6",
DOI = "https://doi.org/10.1073/pnas.0708838104",
ISSN = "0027-8424 (print), 1091-6490 (electronic)",
ISSN-L = "0027-8424",
bibdate = "Fri Jun 3 10:03:23 MDT 2011",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2148367",
abstract = "The concept of pagerank was first started as a way for
determining the ranking of Web pages by Web search
engines. Based on relations in interconnected networks,
pagerank has become a major tool for addressing
fundamental problems arising in general graphs,
especially for large information networks with hundreds
of thousands of nodes. A notable notion of pagerank,
introduced by Brin and Page and denoted by PageRank, is
based on random walks as a geometric sum. In this
paper, we consider a notion of pagerank that is based
on the (discrete) heat kernel and can be expressed as
an exponential sum of random walks. The heat kernel
satisfies the heat equation and can be used to analyze
many useful properties of random walks in a graph. A
local Cheeger inequality is established, which implies
that, by focusing on cuts determined by linear
orderings of vertices using the heat kernel pageranks,
the resulting partition is within a quadratic factor of
the optimum. This is true, even if we restrict the
volume of the small part separated by the cut to be
close to some specified target value. This leads to a
graph partitioning algorithm for which the running time
is proportional to the size of the targeted volume
(instead of the size of the whole graph).",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the National Academy of Sciences of the
United States of America",
journal-URL = "http://www.pnas.org/search",
}
@InProceedings{Constantine:2007:UPC,
author = "Paul G. Constantine and David F. Gleich",
title = "Using polynomial chaos to compute the influence of
multiple random surfers in the {PageRank} model",
crossref = "Bonato:2007:AMW",
pages = "82--95",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-77004-6_7",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
MRclass = "68U35 (60G99 65C05 68W40)",
MRnumber = "MR2505172",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
ZMnumber = "1136.68321",
acknowledgement = ack-nhfb,
}
@InProceedings{Costache:2007:PPB,
author = "Stefania Costache and Wolfgang Nejdl and Raluca Paiu",
editor = "Anonymous",
booktitle = "Proceedings of the 19th International Conference on
Advanced Information Systems Engineering",
title = "Personalizing {PageRank-based} ranking over
distributed collections",
publisher = pub-SV,
address = pub-SV:adr,
pages = "111--126",
year = "2007",
DOI = "https://doi.org/10.1145/511446.511513",
ISBN = "0-7918-4804-3",
ISBN-13 = "978-0-7918-4804-3",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "TA174 .D4623 2007",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "In distributed work environments, where users are
sharing and searching resources, ensuring an
appropriate ranking at remote peers is a key problem.
While this issue has been investigated for federated
libraries, where the exchange of collection specific
information suffices to enable homogeneous TFxIDF
rankings across the participating collections, no
solutions are known for PageRank-based ranking schemes,
important for personalized retrieval on the
desktop.\par
Connected users share fulltext resources and metadata
expressing information about them and connecting them.
Based on which information is shared or private, we
propose several algorithms for computing personalized
PageRank-based rankings for these connected peers. We
discuss which information is needed for the ranking
computation and how Page-Rank values can be estimated
in case of incomplete information. We analyze the
performance of our algorithms through a set of
experiments, and conclude with suggestions for choosing
among these algorithms.",
acknowledgement = ack-nhfb,
keywords = "distributed search; pagerank; personalization;
privacy",
}
@Article{DelCorso:2007:CKS,
author = "Gianna M. {Del Corso} and Antonio Gull{\'\i} and
Francesco Romani",
title = "Comparison of {Krylov} subspace methods on the
{PageRank} problem",
journal = j-J-COMPUT-APPL-MATH,
volume = "210",
number = "1--2",
pages = "159--166",
year = "2007",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2006.10.080",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
MRclass = "65F15 (65Y20)",
MRnumber = "MR2389165 (2009b:65096)",
MRreviewer = "Valeria Ruggiero",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1134.65026",
abstract = "PageRank algorithm plays a very important role in
search engine technology and consists in the
computation of the eigenvector corresponding to the
eigenvalue one of a matrix whose size is now in the
billions. The problem incorporates a parameter @a that
determines the difficulty of the problem. In this
paper, the effectiveness of stationary and
nonstationary methods are compared on some portion of
real web matrices for different choices of @a. We see
that stationary methods are very reliable and more
competitive when the problem is well conditioned, that
is for small values of @a. However, for large values of
the parameter @a the problem becomes more difficult and
methods such as preconditioned BiCGStab or restarted
preconditioned GMRES become competitive with stationary
methods in terms of Mflops count as well as in number
of iterations necessary to reach convergence.",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Djerassi:2007:BRW,
author = "Carl Djerassi",
title = "Book Reviews: When acting speaks louder than words:
Science on Stage: {{\booktitle{From `Doctor Faustus' to
`Copenhagen'}}, by Kirsten Shepherd-Barr.
\booktitle{Google's PageRank and Beyond: The Science of
Search Engine Rankings}, by Amy N. Langville and Carl
D. Meyer. \booktitle{Broken Genius The Rise and Fall of
William Shockley, Creator of the Electronic Age}, by
Joel N. Shurkin}",
journal = j-PHYS-TODAY,
volume = "60",
number = "2",
pages = "63--64",
year = "2007",
CODEN = "PHTOAD",
DOI = "https://doi.org/10.1063/1.2711638",
ISSN = "0031-9228 (print), 1945-0699 (electronic)",
ISSN-L = "0031-9228",
bibdate = "Wed Sep 12 15:15:45 MDT 2012",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/b/bohr-niels.bib;
https://www.math.utah.edu/pub/bibnet/authors/h/heisenberg-werner.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.aip.org/link/phtoad/v60/i2/p63/s1",
acknowledgement = ack-nhfb,
fjournal = "Physics Today",
journal-URL = "http://www.physicstoday.org/",
keywords = "Copenhagen; Michael Frayn; Niels Bohr; Werner
Heisenberg",
}
@Article{Donato:2007:WGH,
author = "Debora Donato and Luigi Laura and Stefano Leonardi and
Stefano Millozzi",
title = "The {Web} as a graph: {How} far we are",
journal = j-TOIT,
volume = "7",
number = "1",
pages = "4:1--4:??",
month = feb,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1145/1189740.1189744",
ISSN = "1533-5399 (print), 1557-6051 (electronic)",
ISSN-L = "1533-5399",
bibdate = "Mon Jun 16 10:57:52 MDT 2008",
bibsource = "http://www.acm.org/pubs/contents/journals/toit/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/toit.bib",
abstract = "In this article we present an experimental study of
the properties of webgraphs. We study a large crawl
from 2001 of 200M pages and about 1.4 billion edges,
made available by the WebBase project at Stanford, as
well as several synthetic ones generated according to
various models proposed recently. We investigate
several topological properties of such graphs,
including the number of bipartite cores and strongly
connected components, the distribution of degrees and
PageRank values and some correlations; we present a
comparison study of the models against these
measures.Our findings are that (i) the WebBase sample
differs slightly from the (older) samples studied in
the literature, and (ii) despite the fact that these
models do not catch all of its properties, they do
exhibit some peculiar behaviors not found, for example,
in the models from classical random graph
theory.Moreover we developed a software library able to
generate and measure massive graphs in secondary
memory; this library is publicy available under the GPL
licence. We discuss its implementation and some
computational issues related to secondary memory graph
algorithms.",
acknowledgement = ack-nhfb,
articleno = "4",
fjournal = "ACM Transactions on Internet Technology (TOIT)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J780",
keywords = "graph structure; models; World-Wide-Web",
}
@Article{Douglis:2007:ECW,
author = "Fred Douglis",
title = "From the {Editor in Chief}: What's Your {PageRank}?",
journal = j-IEEE-INTERNET-COMPUT,
volume = "11",
number = "4",
pages = "3--4",
month = jul # "\slash " # aug,
year = "2007",
CODEN = "IICOFX",
DOI = "https://doi.org/10.1109/MIC.2007.82",
ISSN = "1089-7801",
ISSN-L = "1089-7801",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4270541",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4236",
fjournal = "IEEE Internet Computing",
}
@InProceedings{Du:2007:USF,
author = "Ye Du and Yaoyun Shi and Xin Zhao",
editor = "{ACM}",
booktitle = "AIRWeb; Vol. 215 Proceedings of the 3rd international
workshop on Adversarial information retrieval on the
web",
title = "Using spam farm to boost {PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "29--36",
year = "2007",
DOI = "https://doi.org/10.1145/1062745.1062762",
ISBN = "1-59593-732-3",
ISBN-13 = "978-1-59593-732-2",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Nowadays web spamming has emerged to take the economic
advantage of high search rankings and threatened the
accuracy and fairness of those rankings. Understanding
spamming techniques is essential for evaluating the
strength and weakness of a ranking algorithm, and for
fighting against web spamming. In this paper, we
identify the optimal spam farm structure under some
realistic assumptions in the single target spam farm
model. Our result extends the optimal spam farm claimed
by Gy{\"o}ngyi and Garcia-Molina through dropping the
assumption that leakage is constant. We also
characterize the optimal spam farms under additional
constraints, which the spammer may deploy to disguise
the spam farm by deviating from the unconstrained
optimal structure.",
acknowledgement = ack-nhfb,
keywords = "link spamming; Markov chain; PageRank algorithm",
}
@Article{Eirinaki:2007:WSP,
author = "Magdalini Eirinaki and Michalis Vazirgiannis",
title = "{Web} site personalization based on link analysis and
navigational patterns",
journal = j-TOIT,
volume = "7",
number = "4",
pages = "21:1--21:??",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1145/1278366.1278370",
ISSN = "1533-5399 (print), 1557-6051 (electronic)",
ISSN-L = "1533-5399",
bibdate = "Mon Jun 16 10:58:47 MDT 2008",
bibsource = "http://www.acm.org/pubs/contents/journals/toit/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/toit.bib",
abstract = "The continuous growth in the size and use of the World
Wide Web imposes new methods of design and development
of online information services. The need for predicting
the users' needs in order to improve the usability and
user retention of a Web site is more than evident and
can be addressed by personalizing it. Recommendation
algorithms aim at proposing ``next'' pages to users
based on their current visit and past users'
navigational patterns. In the vast majority of related
algorithms, however, only the usage data is used to
produce recommendations, disregarding the structural
properties of the Web graph. Thus important---in terms
of PageRank authority score---pages may be underrated.
In this work, we present UPR, a PageRank-style
algorithm which combines usage data and link analysis
techniques for assigning probabilities to Web pages
based on their importance in the Web site's
navigational graph. We propose the application of a
localized version of UPR ( l-UPR ) to personalized
navigational subgraphs for online Web page ranking and
recommendation. Moreover, we propose a hybrid
probabilistic predictive model based on Markov models
and link analysis for assigning prior probabilities in
a hybrid probabilistic model. We prove, through
experimentation, that this approach results in more
objective and representative predictions than the ones
produced from the pure usage-based approaches.",
acknowledgement = ack-nhfb,
articleno = "21",
fjournal = "ACM Transactions on Internet Technology (TOIT)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J780",
keywords = "link analysis; Markov models; recommendations;
usage-based PageRank; Web personalization",
}
@Article{Fortunato:2007:LEP,
author = "Santo Fortunato and Mari{\'a}n Bogu{\~n}{\'a} and
Alessandro Flammini and Filippo Menczer",
title = "On local estimations of {PageRank}: a mean field
approach",
journal = j-INTERNET-MATH,
volume = "4",
number = "2--3",
pages = "245--266",
year = "2007",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "60G50 (60J20 68M10)",
MRnumber = "MR2522878",
bibdate = "Wed May 5 19:28:04 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1243430608",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@Article{Fortunato:2007:RWD,
author = "Santo Fortunato and Alessandro Flammini",
title = "Random walks on directed networks: the case of
{PageRank}",
journal = j-INT-J-BIFURC-CHAOS-APPL-SCI-ENG,
volume = "17",
number = "7",
pages = "2343--2353",
year = "2007",
CODEN = "IJBEE4",
DOI = "https://doi.org/10.1142/S0218127407018439",
ISSN = "0218-1274",
MRclass = "60G50 (05C38 68M10); 60G50 05C38 68M10 82B41",
MRnumber = "MR2349743 (2008h:60171)",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1142.68311",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Bifurcation and Chaos in
Applied Sciences and Engineering",
}
@Article{Gleich:2007:APP,
author = "D. F. Gleich and M. Polito",
title = "Approximating personalized {PageRank} with minimal use
of webgraph data",
journal = j-INTERNET-MATH,
volume = "3",
number = "3",
pages = "257--294",
year = "2007",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
bibdate = "Tue Aug 11 16:52:54 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/euclid.im/1204906158",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InCollection{Gleich:2007:TRP,
author = "David Gleich and Peter Glynn and Gene Golub and Chen
Greif",
editor = "A. Frommer and M. W. Mahoney and D. B. Szyld",
booktitle = "{Internationales Begegnungs- und Forschungszentrum
f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany}",
title = "Three results on the {PageRank} vector:
eigenstructure, sensitivity, and the derivative",
publisher = "International Begegnungs- und Forschungszentrum
f{\"u}r Informatik",
address = "Wadern, Germany",
pages = "????",
year = "2007",
ISBN = "????",
ISBN-13 = "????",
LCCN = "????",
bibdate = "Fri Jun 3 10:03:23 MDT 2011",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Dagstuhl seminar proceedings 07071",
URL = "http://drops.dagstuhl.de/opus/volltexte/2007/1061/pdf/07071.GleichDavid.Paper.1061",
acknowledgement = ack-nhfb,
}
@InProceedings{Gori:2007:IRW,
author = "Marco Gori and Augusto Pucci",
editor = "Manuela M. Veloso",
booktitle = "{IJCAI--07, proceedings of the Twentieth International
Joint Conference on Artificial Intelligence: Hyderabad,
India, 6-12 January, 2007}",
title = "{ItemRank}: A random-walk based scoring algorithm for
recommender engines",
publisher = "AAAI Press",
address = "Menlo Park, CA, USA",
pages = "2766--2771",
year = "2007",
ISBN = "1-57735-298-X",
ISBN-13 = "978-1-57735-298-3",
LCCN = "Q335.5 .I55 2007",
bibdate = "Tue Aug 11 16:56:21 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ijcai.org/papers07/Papers/IJCAI07-444.pdf",
acknowledgement = ack-nhfb,
bookpages = "xlvi + 2954 (two volumes)",
xxaddress = pub-MORGAN-KAUFMANN:adr,
xxbooktitle = "Proceedings of the 20th International Joint Conference
on Artificial Intelligence, IJCAI'07, San Francisco,
CA",
xxpublisher = pub-MORGAN-KAUFMANN,
}
@InBook{Gray:2007:IOS,
author = "Andrew P. Gray and Chen Greif and Tracy Lau",
title = "An inner, outer stationary iteration for computing
{PageRank}",
volume = "07071",
publisher = "International Begegnungs- und Forschungszentrum
f{\"u}r Informatik",
address = "Wadern, Germany",
pages = "????",
year = "2007",
ISBN = "????",
ISBN-13 = "????",
LCCN = "????",
bibdate = "Fri Feb 19 15:32:30 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Dagstuhl seminar proceedings",
URL = "http://drops.dagstuhl.de/opus/volltexte/2007/1062/pdf/07071.GreifChen.Paper.1062",
acknowledgement = ack-nhfb,
}
@InProceedings{Guo:2007:MAC,
author = "Ye Guo",
title = "{MixPR} --- An Approach of Combining Content and Links
of {Web} Page[s]",
crossref = "Lei:2007:FPF",
volume = "2",
pages = "456--460",
year = "2007",
DOI = "https://doi.org/10.1109/FSKD.2007.407",
bibdate = "Thu May 06 15:23:46 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Guo:2007:PPW,
author = "Yong Zhen Guo and Kotagiri Ramamohanarao and Laurence
A. F. Park",
booktitle = "{IEEE\slash WIC\slash ACM International Conference on
Web Intelligence}",
title = "Personalized {PageRank} for {Web} Page Prediction
Based on Access Time-Length and Frequency",
crossref = "Lin:2007:PIW",
pages = "687--690",
year = "2007",
DOI = "https://doi.org/10.1109/WI.2007.58",
ISBN = "0-7695-3026-5",
ISBN-13 = "978-0-7695-3026-0",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4427174",
abstract = "Web page prefetching techniques are used to address
the access latency problem of the Internet. To perform
successful prefetching, we must be able to predict the
next set of pages that will be accessed by users. The
PageRank algorithm used by Google is able to compute
the popularity of a set of Web pages based on their
link structure. In this paper, a novel PageRank-like
algorithm is proposed for conducting Web page
prediction. Two biasing factors are adopted to
personalize PageRank, so that it favors the pages that
are more important to users. One factor is the length
of time spent on visiting a page and the other is the
frequency that a page was visited. The experiments
conducted show that using these two factors
simultaneously to bias PageRank results in more
accurate Web page prediction.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043",
}
@Article{He:2007:CSW,
author = "Xiaofei He and Deng Cai and Ji-Rong Wen and Wei-Ying
Ma and Hong-Jiang Zhang",
title = "Clustering and searching {WWW} images using link and
page layout analysis",
journal = j-TOMCCAP,
volume = "3",
number = "2",
pages = "10:1--10:??",
month = may,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1145/1230812.1230816",
ISSN = "1551-6857 (print), 1551-6865 (electronic)",
ISSN-L = "1551-6857",
bibdate = "Mon Jun 16 17:10:04 MDT 2008",
bibsource = "http://www.acm.org/pubs/contents/journals/tomccap/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/tomccap.bib",
abstract = "Due to the rapid growth of the number of digital
images on the Web, there is an increasing demand for an
effective and efficient method for organizing and
retrieving the available images. This article describes
iFind, a system for clustering and searching WWW
images. By using a vision-based page segmentation
algorithm, a Web page is partitioned into blocks, and
the textual and link information of an image can be
accurately extracted from the block containing that
image. The textual information is used for image
indexing. By extracting the page-to-block,
block-to-image, block-to-page relationships through
link structure and page layout analysis, we construct
an image graph. Our method is less sensitive to noisy
links than previous methods like PageRank, HITS, and
PicASHOW, and hence the image graph can better reflect
the semantic relationship between images. Using the
notion of Markov Chain, we can compute the limiting
probability distributions of the images, ImageRanks,
which characterize the importance of the images. The
ImageRanks are combined with the relevance scores to
produce the final ranking for image search. With the
graph models, we can also use techniques from spectral
graph theory for image clustering and embedding, or 2-D
visualization. Some experimental results on 11.6
million images downloaded from the Web are provided in
the article.",
acknowledgement = ack-nhfb,
articleno = "10",
fjournal = "ACM Transactions on Multimedia Computing,
Communications, and Applications",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J961",
keywords = "image clustering; image search; link analysis; Web
mining",
}
@Article{Horn:2007:GSP,
author = "Roger A. Horn and Stefano Serra-Capizzano",
title = "A general setting for the parametric {Google} matrix",
journal = j-INTERNET-MATH,
volume = "3",
number = "4",
pages = "385--411",
month = "????",
year = "2007",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
bibdate = "Tue Aug 11 17:04:27 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/euclid.im/1227025007",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Hussain:2007:SARa,
author = "F. K. Hussain and E. Chang and O. K. Hussain",
title = "State of the art review of the existing {PageRank}
based algorithms for trust computation",
crossref = "Dini:2007:SIC",
pages = "75--75",
year = "2007",
DOI = "https://doi.org/10.1109/ICSNC.2007.78",
bibdate = "Thu May 06 15:25:50 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In this paper we present a state of the art review of
PageRank based approaches for trust and reputation
computation. We divide the approaches that make use of
PageRank method for trust and reputation computation,
into six different classes. Each of the six classes is
discussed in this paper.",
acknowledgement = ack-nhfb,
}
@InProceedings{Hussain:2007:SARb,
author = "F. K. Hussain and E. Chang and O. K. Hussain",
title = "State of the art review of the existing {PageRank}
based algorithms for trust and reputation computation",
crossref = "Ramakrishnan:2007:PSI",
pages = "43--43",
year = "2007",
DOI = "https://doi.org/10.1109/ICIMP.2007.44",
bibdate = "Thu May 06 16:11:46 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Article{Ipsen:2007:PCS,
author = "Ilse C. F. Ipsen and Teresa M. Selee",
title = "{PageRank} computation, with special attention to
dangling nodes",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "29",
number = "4",
pages = "1281--1296",
month = nov,
year = "2007",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/060664331",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
MRclass = "65C40 (15A06 15A18 68M10 68P20)",
MRnumber = "MR2369296 (2009a:65013)",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1156.65038",
abstract = "We present a simple algorithm for computing the
PageRank (stationary distribution) of the stochastic
Google matrix $G$. The algorithm lumps all dangling
nodes into a single node. We express lumping as a
similarity transformation of $G$ and show that the
PageRank of the nondangling nodes can be computed
separately from that of the dangling nodes. The
algorithm applies the power method only to the smaller
lumped matrix, but the convergence rate is the same as
that of the power method applied to the full matrix
$G$. The efficiency of the algorithm increases as the
number of dangling nodes increases. We also extend the
expression for PageRank and the algorithm to more
general Google matrices that have several different
dangling node vectors, when it is required to
distinguish among different classes of dangling nodes.
We also analyze the effect of the dangling node vector
on the PageRank and show that the PageRank of the
dangling nodes depends strongly on that of the
nondangling nodes but not vice versa. Last we present a
Jordan decomposition of the Google matrix for the
(theoretical) extreme case when all Web pages are
dangling nodes.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
keywords = "Google; Jordan decomposition; lumping; power method;
rank-one matrix; similarity transformation; stationary
distribution; stochastic matrix",
}
@InProceedings{Jiang:2007:SBC,
author = "Qiancheng Jiang and Yan Zhang",
title = "{SiteRank}-Based Crawling Ordering Strategy for Search
Engines",
crossref = "Miyazaki:2007:CPI",
pages = "259--263",
year = "2007",
DOI = "https://doi.org/10.1109/CIT.2007.35",
bibdate = "Thu May 06 15:48:26 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Kao:2007:FPC,
author = "Hung-Yu Kao and Seng-Feng Lin",
booktitle = "{IEEE\slash WIC\slash ACM International Conference on
Web Intelligence}",
title = "A Fast {PageRank} Convergence Method based on the
Cluster Prediction",
crossref = "Lin:2007:PIW",
pages = "593--599",
year = "2007",
DOI = "https://doi.org/10.1109/WI.2007.129",
ISBN = "0-7695-3026-5",
ISBN-13 = "978-0-7695-3026-0",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4427158",
abstract = "In recent years, search engines have already played
the key roles among Web applications, and link analysis
algorithms are the major methods to measure the
important values of Web pages. These algorithms employ
the conventional flat Web graph built by Web pages and
link relations of Web pages to obtain the relative
importance of Web objects. Previous researches have
observed that PageRank-like link analysis algorithms
have a bias against newly created Web pages. A new
ranking algorithm called Page Quality was then proposed
to solve this issue. Page Quality predicates future
ranking values by the difference rate between the
current ranking value and the previous ranking value.
In this paper, we propose a new algorithm called DRank
to diminish the bias of PageRank-like link analysis
algorithms, and attain the better performance than Page
Quality. In this algorithm, we model Web graph as a
three-layer graph which includes Host Graph, Directory
Graph and Page Graph by using the hierarchical
structure of URLs and the structure of link relation of
Web pages. We calculate the importance of Hosts,
Directories and Pages by weighted graph we built and
then the clustering distribution of PageRank values of
pages within directories is observed. We can then
predicate the more accurate values of page importance
to diminish the bias of newly created pages by the
clustering characteristic of PageRank. Experiment
results show that DRank algorithm works well on
predicating future ranking values of pages and
outperform Page Quality.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043",
}
@InProceedings{Kohlschutter:2007:UAT,
author = "Christian Kohlsch{\"u}tter and Paul-Alexandru Chirita
and Wolfgang Nejdl",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceedings of
the 16th international conference on World Wide Web",
title = "Utility analysis for topically biased {PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "1211--1212",
year = "2007",
DOI = "https://doi.org/10.1145/511446.511513",
ISBN = "1-59593-654-8",
ISBN-13 = "978-1-59593-654-7",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "PageRank is known to be an efficient metric for
computing general document importance in the Web. While
commonly used as a one-size-fits-all measure, the
ability to produce topically biased ranks has not yet
been fully explored in detail. In particular, it was
still unclear to what granularity of 'topic' the
computation of biased page ranks makes sense. In this
paper we present the results of a thorough quantitative
and qualitative analysis of biasing PageRank on Open
Directory categories. We show that the MAP quality of
Biased PageRank generally increases with the ODP level
up to a certain point, thus sustaining the usage of
more specialized categories to bias PageRank on, in
order to improve topic specific search.",
acknowledgement = ack-nhfb,
keywords = "biased PageRank; open directory; personalized search",
}
@InBook{Kollias:2007:APC,
author = "Giorgos Kollias and Efstratios Gallopoulos",
title = "Asynchronous {PageRank} computation in an interactive
multithreading environment",
volume = "07071",
publisher = "International Begegnungs- und Forschungszentrum
f{\"u}r Informatik",
address = "Wadern, Germany",
pages = "????",
year = "2007",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Feb 19 15:32:30 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Dagstuhl seminar proceedings",
URL = "http://drops.dagstuhl.de/opus/volltexte/2007/1065/pdf/07071.KolliasGiorgios.Paper.1065",
acknowledgement = ack-nhfb,
}
@Article{Lee:2007:TSA,
author = "Chris P. Lee and Gene H. Golub and Stefanos A.
Zenios",
title = "A two-stage algorithm for computing {PageRank} and
multistage generalizations",
journal = j-INTERNET-MATH,
volume = "4",
number = "4",
pages = "299--327",
year = "2007",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68M11",
MRnumber = "MR2522947",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1243430809",
abstract = "The PageRank model pioneered by Google is the most
common approach for generating web search results. We
present a two-stage algorithm for computing the
PageRank vector where the algorithm exploits the
lumpability of the underlying Markov chain. We make
three contributions. First, the algorithm speeds up the
PageRank calculation significantly. With web graphs
having millions of webpages, the speed-up is typically
in the two- to three-fold range. The algorithm can also
embed other acceleration methods such as quadratic
extrapolation, the Gauss-Seidel method, or the
Biconjugate gradient stable method for an even greater
speed-up; cumulative speed-up is as high as 7 to 14
times. The second contribution relates to the handling
of dangling nodes. Conventionally, dangling nodes are
included only towards the end of the computation. While
this approach works reasonably well, it can fail in
extreme cases involving aggressive personalization. We
prove that our algorithm is the generally correct way
of handling dangling nodes using probabilistic
arguments. We also discuss variants of our algorithm,
including a multistage extension for calculating a
generalized version of the PageRank model where
different personalization vectors are used for webpages
of different classes. The ability to form class
associations may be useful for building more refined
models of web traffic.",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Li:2007:HCN,
author = "Cun-he Li and Ke-qiang Lu",
title = "Hyperlink Classification: a New Approach to Improve
{PageRank}",
crossref = "Tjoa:2007:DIC",
pages = "274--277",
year = "2007",
DOI = "https://doi.org/10.1109/DEXA.2007.14",
ISBN = "0-7695-2932-1, 0-7695-2932-1",
ISBN-13 = "978-0-7695-2932-5, 978-0-7695-2932-5",
LCCN = "QA76.9.D3",
bibdate = "Fri Feb 19 18:23:12 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4312900",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4312838",
}
@Article{Litvak:2007:DPW,
author = "N. Litvak and W. R. W. Scheinhardt and Y. Volkovich",
title = "{In-Degree} and {PageRank}: why do they follow similar
power laws?",
journal = j-INTERNET-MATH,
volume = "4",
number = "2--3",
pages = "175--198",
year = "2007",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "62H99 (62E15 62E17 62P99 68M10)",
MRnumber = "MR2522875 (2010f:62177)",
MRreviewer = "Pranesh Kumar",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1243430605",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Liu:2007:EBE,
author = "Maofu Liu and Wenjie Li and Mingli Wu and Hujun Hu",
title = "Event-Based Extractive Summarization Using Event
Semantic Relevance from External Linguistic Resource",
crossref = "Ock:2007:ASI",
pages = "117--122",
year = "2007",
DOI = "https://doi.org/10.1109/ALPIT.2007.9",
bibdate = "Thu May 06 16:49:34 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{Liu:2007:KEU,
author = "Jianyi Liu and Jinghua Wang",
title = "Keyword Extraction Using Language Network",
crossref = "IEEE:2007:ICN",
pages = "129--134",
year = "2007",
DOI = "https://doi.org/10.1109/NLPKE.2007.4368023",
bibdate = "Thu May 06 15:29:51 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Mason:2007:WMF,
author = "Zachary Mason",
title = "{WordRank}: a Method for Finding Search-Ad Keywords
for {Internet} Merchants",
crossref = "Clifton:2006:SIC",
pages = "12--12",
year = "2007",
DOI = "https://doi.org/10.1109/ICIW.2007.73",
bibdate = "Thu May 06 16:27:14 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Melucci:2007:PWO,
author = "Massimo Melucci and Luca Pretto",
editor = "Giambattista Amati and Claudio Carpineto and Giovanni
Romano",
booktitle = "{Advances in information retrieval: 29th European
Conference on IR Research, ECIR 2007, Rome, Italy,
April 2-5, 2007: proceedings}",
title = "{PageRank}: when order changes",
publisher = pub-SV,
address = pub-SV:adr,
pages = "581--588",
year = "2007",
DOI = "https://doi.org/10.1145/1060745.1060827",
ISBN = "3-540-71494-4",
ISBN-13 = "978-3-540-71494-1",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "As PageRank is a ranking algorithm, it is of prime
interest to study the order induced by its values on
webpages. In this paper a thorough mathematical
analysis of PageRank-induced order changes when the
damping factor varies is provided. Conditions that do
not allow variations in the order are studied, and the
mechanisms that make the order change are
mathematically investigated. Moreover the influence on
the order of a truncation in the actual computation of
PageRank through a power series is analysed.
Experiments carried out on a large Web digraph to
integrate the mathematical analysis show that PageRank
-- while working on a real digraph -- tends to hinder
variations in the order of large rankings, presenting a
high stability in its induced order both in the face of
large variations of the damping factor value and in the
face of truncations in its computation.",
acknowledgement = ack-nhfb,
}
@InProceedings{Mousavi:2007:CWU,
author = "H. Mousavi and M. E. Rafiei and A. Movaghar",
title = "Characterizing the {Web} Using a New Uniform Sampling
Approach",
crossref = "IEEE:2007:ICC",
pages = "1--5",
year = "2007",
DOI = "https://doi.org/10.1109/COMSWA.2007.382558",
bibdate = "Thu May 06 16:46:35 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Najork:2007:HWH,
author = "Marc A. Najork and Hugo Zaragoza and Michael J.
Taylor",
editor = "Wessel Kraaij and Arjen P. de Vries",
booktitle = "{Proceedings of the 30th Annual International ACM
SIGIR Conference on Research and Development in
Information Retrieval, SIGIR2007. Amsterdam (the
Netherlands), July 23--27, 2007}",
title = "{HITS} on the web: How does it compare?",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "471--478",
year = "2007",
DOI = "https://doi.org/10.1145/1277741.1277823",
ISBN = "1-59593-597-5",
ISBN-13 = "978-1-59593-597-7",
LCCN = "Z699.A1",
bibdate = "Tue Aug 11 17:30:19 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1145/1277741",
bookpages = "928",
}
@InProceedings{Nakakubo:2007:WPS,
author = "H. Nakakubo and S. Nakajima and K. Hatano and J.
Miyazaki and S. Uemura",
title = "{Web} Page Scoring Based on Link Analysis of {Web}
Page Sets",
crossref = "Tjoa:2007:DIC",
pages = "269--273",
year = "2007",
DOI = "https://doi.org/10.1109/DEXA.2007.126",
bibdate = "Thu May 06 15:51:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Nan:2007:ENI,
author = "He Nan and Gan Wen-yan and Li De Yi",
title = "Evaluate Nodes Importance in the Network Using Data
Field Theory",
crossref = "Na:2007:IIC",
pages = "1225--1234",
year = "2007",
DOI = "https://doi.org/10.1109/ICCIT.2007.88",
bibdate = "Thu May 06 16:40:05 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank",
}
@InProceedings{Nie:2007:CSP,
author = "Lan Nie and Baoning Wu and Brian D. Davison",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceedings of
the 16th international conference on World Wide Web",
title = "A cautious surfer for {PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "1119--1120",
year = "2007",
DOI = "https://doi.org/10.1145/1149121.1149124",
ISBN = "1-59593-654-8",
ISBN-13 = "978-1-59593-654-7",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "This work proposes a novel cautious surfer to
incorporate trust into the process of calculating
authority for web pages. We evaluate a total of sixty
queries over two large, real-world datasets to
demonstrate that incorporating trust can improve
PageRank's performance.",
acknowledgement = ack-nhfb,
keywords = "authority; ranking performance; spam; trust; web
search engine",
}
@Article{Pedroche:2007:MCP,
author = "Francisco Pedroche",
title = "Methods of calculating the {PageRank} vector",
journal = "Bol. Soc. Esp. Mat. Apl. S$\vec{\rm e}$MA",
volume = "39",
pages = "7--30",
year = "2007",
CODEN = "????",
ISSN = "1575-9822",
MRclass = "15A18 (65F10 65F15)",
MRnumber = "MR2406972 (2009c:15016)",
MRreviewer = "Juan Manuel Pe{\~n}a",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Bolet\'\i n de la Sociedad Espa\~nola de Matem\'atica
Aplicada. S$\vec{\rm e}$MA",
}
@InProceedings{Qiao:2007:EAP,
author = "Jonathan Qiao and Brittany Jones and Stacy Thrall",
editor = "Yong Shi and others",
booktitle = "Proceedings of the 7th international conference on
Computational Science, Part I: ICCS 2007",
title = "An Efficient Algorithm and Its Parallelization for
Computing {PageRank}",
volume = "4487--4490",
publisher = pub-SV,
address = pub-SV:adr,
pages = "237--244",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-72584-8_31",
ISBN = "3-540-72583-0",
ISBN-13 = "978-3-540-72583-1",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
bibdate = "Sat May 8 18:33:07 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "In this paper, an efficient algorithm and its
parallelization to compute PageRank are proposed. There
are existing algorithms to perform such tasks. However,
some algorithms exclude dangling nodes which are an
important part and carry important information of the
web graph. In this work, we consider dangling nodes as
regular web pages without changing the web graph
structure and therefore fully preserve the information
carried by them. This differs from some other
algorithms which include dangling nodes but treat them
differently from regular pages for the purpose of
efficiency. We then give an efficient algorithm with
negligible overhead associated with dangling node
treatment. Moreover, the treatment poses little
difficulty in the parallelization of the algorithm.",
acknowledgement = ack-nhfb,
keywords = "algorithm; dangling nodes; PageRank; power method",
}
@InProceedings{Rungsawang:2007:BLF,
author = "Arnon Rungsawang and Komthorn Puntumapon and Bundit
Manaskasemsak",
booktitle = "{AINA '07: 21st International Conference on Advanced
Information Networking and Applications (2007)}",
title = "Un-biasing the Link Farm Effect in {PageRank}
Computation",
crossref = "IEEE:2007:ICA",
pages = "924--931",
year = "2007",
DOI = "https://doi.org/10.1109/AINA.2007.143",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4220990",
abstract = "Link analysis is a critical component of current
Internet search engines' results ranking software,
which determines the ordering of query results returned
to the user. The ordering of query results can have an
enormous impact on web traffic and the resulting
business activity of an enterprise; hence businesses
have a strong interest in having their web pages highly
ranked in search engine results. This has led to
attempts to artificially inflate page ranks by spamming
the link structure of the web. Building an artificial
condensed link structure called a 'link farm' is one
technique to influence a page ranking system, such as
the popular PageRank algorithm. In this paper, we
present an approach to remove the bias due to link
farms from PageRank computation. We propose a method to
first measure the PageRank weight accumulated by link
farms, and then distribute the weight to other web
pages by a modification of the transition matrix in the
standard PageRank algorithm. We present results of a
selected web graph that is manually spammed. The
results show that the proposed approach can effectively
reduce the bias from link farms in PageRank
computation.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4220856",
}
@InProceedings{Schatten:2007:OFS,
author = "M. Schatten and M. Zugaj",
title = "Organizing a Fishnet Structure",
crossref = "Luzar-Stiffler:2007:PII",
pages = "81--86",
year = "2007",
DOI = "https://doi.org/10.1109/ITI.2007.4283748",
bibdate = "Thu May 06 15:05:59 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Shih:2007:VAR,
author = "Huang-Chia Shih and Chung-Lin Huang and Jenq-Neng
Hwang",
title = "Video Attention Ranking using Visual and Contextual
Attention Model for Content-based Sports Videos
Mining",
crossref = "IEEE:2007:IWM",
pages = "414--417",
year = "2007",
DOI = "https://doi.org/10.1109/MMSP.2007.4412904",
bibdate = "Thu May 06 15:01:22 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Volkovich:2007:DFB,
author = "Yana Volkovich and Nelly Litvak and Debora Donato",
title = "Determining factors behind the {PageRank} log-log
plot",
crossref = "Bonato:2007:AMW",
pages = "108--123",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-77004-6_9",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
MRclass = "68U35 (05C90 68M10 68R10 91D30)",
MRnumber = "MR2504910",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
ZMnumber = "1136.68339",
acknowledgement = ack-nhfb,
}
@Article{Volkovich:2007:SMW,
author = "Y. Volkovich and D. Donato and N. Litvak",
title = "Stochastic models for {Web} ranking",
journal = j-SIGMETRICS,
volume = "35",
number = "3",
pages = "53--53",
month = dec,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1145/1328690.1328713",
ISSN = "0163-5999 (print), 1557-9484 (electronic)",
ISSN-L = "0163-5999",
bibdate = "Fri Jun 27 09:42:53 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/sigmetrics.bib",
abstract = "Web search engines need to deal with hundreds and
thousands of pages which are relevant to a user's
query. Listing them in the right order is an important
and non-trivial task. Thus Google introduced {\em
PageRank\/} [1] as a popularity measure for Web pages.
Besides its primary application in search engines,
PageRank also became a major method for evaluating
importance of nodes in different informational networks
and database systems.",
acknowledgement = ack-nhfb,
fjournal = "ACM SIGMETRICS Performance Evaluation Review",
journal-URL = "http://portal.acm.org/toc.cfm?id=J618",
}
@Article{Walker:2007:RSP,
author = "Dylan Walker and Huafeng Xie and Koon-Kiu Yan and
Sergei Maslov",
title = "Ranking scientific publications using a model of
network traffic",
journal = j-J-STAT-MECH-THEORY-EXP,
volume = "6",
number = "??",
pages = "P06010",
month = jun,
year = "2007",
CODEN = "JSMTC6",
DOI = "https://doi.org/10.1088/1742-5468/2007/06/P06010",
ISSN = "1742-5468",
bibdate = "Tue Aug 11 17:42:29 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://arxiv.org/abs/physics/0612122;
http://iopscience.iop.org/1742-5468/2007/06/P06010/fulltext/",
acknowledgement = ack-nhfb,
fjournal = "Journal of Statistical Mechanics: Theory and
Experiment",
journal-URL = "http://iopscience.iop.org/1742-5468/",
keywords = "CiteRank",
}
@InProceedings{Wang:2007:KEB,
author = "Jinghua Wang and Jianyi Liu and Cong Wang",
editor = "Zhi-Hua Zhou and Hang Li and Qiang Yang",
booktitle = "{PPAKDD'07: Proceedings of the 11th Pacific-Asia
Conference on Advances in Knowledge Discovery and Data
Mining}",
title = "Keyword extraction based on {PageRank}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "857--864",
year = "2007",
DOI = "https://doi.org/10.3115/1219044.1219064",
ISBN = "3-540-71700-5",
ISBN-13 = "978-3-540-71700-3",
bibdate = "Sat May 8 18:33:07 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNAI,
abstract = "Keywords are viewed as the words that represent the
topic and the content of the whole text. Keyword
extraction is an important technology in many areas of
document processing, such as text clustering, text
summarization, and text retrieval. This paper provides
a keyword extraction algorithm based on WordNet and
PageRank. Firstly, a text is represented as a rough
undirected weighted semantic graph with WordNet, which
defines synsets as vertices and relations of vertices
as edges, and assigns the weight of edges with the
relatedness of connected synsets. Then we apply
UW-PageRank in the rough graph to do word sense
disambiguation, prune the graph, and finally apply
UW-PageRank again on the pruned graph to extract
keywords. The experimental results show our algorithm
is practical and effective.",
acknowledgement = ack-nhfb,
}
@InProceedings{Wicks:2007:MEP,
author = "John R. Wicks and Amy Greenwald",
editor = "Wessel Kraaij and Arjen P. de Vries",
booktitle = "{Proceedings of the 30th Annual International ACM
SIGIR Conference on Research and Development in
Information Retrieval, SIGIR2007. Amsterdam (the
Netherlands), July 23--27, 2007}",
title = "More efficient parallel computation of {PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "861--862",
year = "2007",
ISBN = "1-59593-597-5",
ISBN-13 = "978-1-59593-597-7",
LCCN = "Z699.A1",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1145/1277741",
bookpages = "928",
keywords = "pagerank; power iteration; web graph",
}
@InProceedings{Wicks:2007:PCP,
author = "John Wicks and Amy Greenwald",
title = "Parallelizing the computation of {PageRank}",
crossref = "Bonato:2007:AMW",
pages = "202--208",
year = "2007",
DOI = "https://doi.org/10.1007/978-3-540-77004-6_17",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
MRclass = "68U35 (68M10 68R10 68W10)",
MRnumber = "MR2504918",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
ZMnumber = "1136.68340",
acknowledgement = ack-nhfb,
}
@Article{Wu:2007:PAA,
author = "Gang Wu and Yimin Wei",
title = "A Power-{Arnoldi} algorithm for computing {PageRank}",
journal = j-NUM-LIN-ALG-APPL,
volume = "14",
number = "7",
pages = "521--546",
year = "2007",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.531",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
ISSN-L = "1070-5325",
MRclass = "65F15; 65F15 65F10",
MRnumber = "MR2348401 (2009a:65097)",
MRreviewer = "Cristina Tablino Possio",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "05596057",
acknowledgement = ack-nhfb,
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506",
}
@InProceedings{Wu:2007:SAR,
author = "Gang Wu and Juanzi Li",
title = "{SWRank}: An Approach for Ranking {Semantic Web}
Reversely and Consistently",
crossref = "IEEE:2007:PTI",
pages = "116--121",
year = "2007",
DOI = "https://doi.org/10.1109/SKG.2007.81",
bibdate = "Thu May 06 15:38:58 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Yang:2007:BBS,
author = "Lun Yang and Bin Wang and Gongli Xia and Zhenhua Xia
and Langlai Xu",
booktitle = "{BIC-TA 2007: Second International Conference on
Bio-Inspired Computing: Theories and Applications}",
title = "Bibliomics-based Selection of Analgesics Targets
through {Google}-{PageRank}-like Algorithm",
crossref = "IEEE:2007:SICa",
pages = "98--101",
year = "2007",
DOI = "https://doi.org/10.1109/BICTA.2007.4806427",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4806427",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4801442",
}
@InProceedings{Yang:2007:DPP,
author = "Haixuan Yang and Irwin King and Michael R. Lyu",
editor = "Wessel Kraaij and Arjen P. de Vries",
booktitle = "{Proceedings of the 30th Annual International ACM
SIGIR Conference on Research and Development in
Information Retrieval, SIGIR2007. Amsterdam (the
Netherlands), July 23--27, 2007}",
title = "{DiffusionRank}: A possible penicillin for web
spamming",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "431--438",
year = "2007",
DOI = "https://doi.org/10.1145/1277741.1277815",
ISBN = "1-59593-597-5",
ISBN-13 = "978-1-59593-597-7",
LCCN = "Z699.A1",
bibdate = "Tue Aug 11 17:50:04 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1145/1277741",
bookpages = "928",
}
@InProceedings{Yuan:2007:IPF,
author = "Fuyong Yuan and Chunxia Yin and Jian Liu",
editor = "{IEEE}",
booktitle = "{SNPD 2007: Eighth ACIS International Conference on
Software Engineering, Artificial Intelligence,
Networking, and Parallel\slash Distributed Computing}",
title = "Improvement of {PageRank} for Focused Crawler",
volume = "2",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "797--802",
year = "2007",
DOI = "https://doi.org/10.1109/SNPD.2007.458",
ISBN = "0-7695-2909-7",
ISBN-13 = "978-0-7695-2909-7",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4287791",
abstract = "The rapid growth of the World-Wide Web poses
unprecedented scaling challenges for general-purpose
crawlers. Focused crawler is developed to collect
relevant web pages of interested topics form the
Internet. The PageRank algorithm is used in ranking web
pages. It estimates the page's authority by taking into
account the link structure of the Web. However, it
assigns each outlink the same weight and is independent
of topics, resulting in topic-drift. In this paper, we
proposed an improved PageRank algorithm, which we
called 'T-PageRank', and it based on 'topical random
surfer'. The experiment in focused crawler using the
T-PageRank has better performance than the Breath-first
and PageRank algorithms.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4287452",
keywords = "focused crawler; PageRank; T-PageRank; topical random
surfer",
}
@InProceedings{Yuan:2007:PFC,
author = "Fuyong Yuan and Chunxia Yin and Jian Liu",
title = "{PageRank} for Focused Crawler",
crossref = "Feng:2007:EAI",
pages = "797--802",
year = "2007",
DOI = "https://doi.org/10.1109/SNPD.2007.458",
bibdate = "Fri Feb 19 18:09:30 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4287452",
}
@InProceedings{Yue:2007:UGM,
author = "BaoJun Yue and Heng Liang and Fengshan Bai",
title = "Understanding the {GeneRank} Model",
crossref = "IEEE:2007:BBE",
pages = "248--251",
year = "2007",
DOI = "https://doi.org/10.1109/ICBBE.2007.67",
bibdate = "Thu May 06 16:52:48 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Zhang:2007:AIP,
author = "Yulian Zhang and Chunxia Yin and Fuyong Yuan",
booktitle = "{FSKD 2007: Fourth International Conference on Fuzzy
Systems and Knowledge Discovery}",
title = "An Application of Improved {PageRank} in Focused
Crawler",
crossref = "Lei:2007:FPF",
volume = "2",
pages = "331--335",
year = "2007",
DOI = "https://doi.org/10.1109/FSKD.2007.142",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4406097",
abstract = "The focused crawler of a special-purpose search engine
aims to selectively seek out pages that are relevant to
a pre-defined set of topics, rather than to exploit all
regions of the Web. The PageRank algorithm is often
used in ranking web pages, and it is also used in URL
ordering for focused crawler. It estimates the page's
authority by taking into account the link structure of
the Web. However, it assigns each outlink the same
weight and is independent of topics, resulting in
topic-drift. In this paper, we propose an improved
PageRank algorithm, which we called 'To-PageRank', and
then we present a crawling strategy using the
To-PageRank algorithm combining with the topic
similarity of the hyperlink metadata. The experiment in
focused crawler shows that the new improved crawling
strategy has better performance than the Breath-first
and PageRank algorithms.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4405868",
}
@InProceedings{Zhang:2007:SPW,
author = "Li Zhang and Tao Qin and Tie-Yan Liu and Ying Bao and
Hang Li",
editor = "Giambattista Amati and Claudio Carpineto and Giovanni
Romano",
booktitle = "{Advances in information retrieval: 29th European
Conference on IR Research, ECIR 2007, Rome, Italy,
April 2-5, 2007: proceedings}",
title = "{$N$}-step {PageRank} for {Web} search",
publisher = pub-SV,
address = pub-SV:adr,
pages = "653--660",
year = "2007",
DOI = "https://doi.org/10.1145/324133.324140",
ISBN = "3-540-71494-4",
ISBN-13 = "978-3-540-71494-1",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "PageRank has been widely used to measure the
importance of web pages based on their interconnections
in the web graph. Mathematically speaking, PageRank can
be explained using a Markov random walk model, in which
only the direct outlinks of a page contribute to its
transition probability. In this paper, we propose
improving the PageRank algorithm by looking N -step
ahead when constructing the transition probability
matrix. The motivation comes from the similar 'looking
N -step ahead' strategy that is successfully used in
computer chess. Specifically, we assume that if the
random surfer knows the N -step outlinks of each web
page, he/she can make a better decision on choosing
which page to navigate for the next time. It is clear
that the classical PageRank algorithm is a special case
of our proposed N -step PageRank method. Experimental
results on the dataset of TREC Web track show that our
proposed algorithm can boost the search accuracy of
classical PageRank by more than 15\% in terms of mean
average precision.",
acknowledgement = ack-nhfb,
}
@InProceedings{Zhou:2007:CRA,
author = "Ding Zhou and S. A. Orshanskiy and Hongyuan Zha and C.
L. Giles",
title = "Co-ranking Authors and Documents in a Heterogeneous
Network",
crossref = "Ramakrishnan:2007:PSI",
pages = "739--744",
year = "2007",
DOI = "https://doi.org/10.1109/ICDM.2007.57",
bibdate = "Fri May 07 17:05:21 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Article{Andersen:2008:LCP,
author = "Reid Andersen and Christian Borgs and Jennifer Chayes
and John Hopcroft and Vahab Mirrokni and Shang-Hua
Teng",
title = "Local computation of {PageRank} contributions",
journal = j-INTERNET-MATH,
volume = "5",
number = "1--2",
pages = "23--45",
year = "2008",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68R10 (05C85 68M11)",
MRnumber = "MR2560261",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1259158596",
ZMnumber = "1136.68316",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@Article{Andersen:2008:LPD,
author = "Reid Andersen and Fan Chung and Kevin Lang",
title = "Local partitioning for directed graphs using
{PageRank}",
journal = j-INTERNET-MATH,
volume = "5",
number = "1--2",
pages = "3--22",
year = "2008",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68R10 (05C20 05C70 68M11)",
MRnumber = "MR2560260",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1259158595",
ZMnumber = "1136.68317",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Andersen:2008:RPL,
author = "Reid Andersen and Christian Borgs and Jennifer Chayes
and John Hopcroft and Kamal Jain and Vahab Mirrokni and
Shanghua Teng",
editor = "{ACM}",
booktitle = "AIRWeb; Vol. 295 Proceedings of the 4th international
workshop on Adversarial information retrieval on the
web",
title = "Robust {PageRank} and locally computable spam
detection features",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "69--76",
year = "2008",
DOI = "https://doi.org/10.1145/1244408.1244413",
ISBN = "1-60558-159-3",
ISBN-13 = "978-1-60558-159-0",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Since the link structure of the web is an important
element in ranking systems on search engines, web
spammers widely use the link structure of the web to
increase the rank of their pages. Various link-based
features of web pages have been introduced and have
proven effective at identifying link spam. One
particularly successful family of features (as
described in the SpamRank algorithm), is based on
examining the sets of pages that contribute most to the
PageRank of a given vertex, called supporting sets. In
a recent paper, the current authors described an
algorithm for efficiently computing, for a single
specified vertex, an approximation of its supporting
sets. In this paper, we describe several link-based
spam-detection features, both supervised and
unsupervised, that can be derived from these
approximate supporting sets. In particular, we examine
the size of a node's supporting sets and the
approximate l 2 norm of the PageRank contributions from
other nodes. As a supervised feature, we examine the
composition of a node's supporting sets. We perform
experiments on two labeled real data sets to
demonstrate the effectiveness of these features for
spam detection, and demonstrate that these features can
be computed efficiently. Furthermore, we design a
variation of PageRank (called Robust PageRank) that
incorporates some of these features into its ranking,
argue that this variation is more robust against link
spam engineering, and give an algorithm for
approximating Robust PageRank.",
acknowledgement = ack-nhfb,
keywords = "directed graphs; graph algorithms; link spam; local
algorithms; PageRank; unsupervised learning",
}
@PhdThesis{Augeri:2008:GIP,
author = "Christopher J. Augeri",
title = "On graph isomorphism and the {PageRank} algorithm",
type = "{Ph.D.} dissertation",
school = "Air Force Institute of Technology",
address = "Wright--Patterson Air Force Base, OH, USA",
pages = "xiv + 137",
month = sep,
year = "2008",
ISBN = "0-549-92090-0",
ISBN-13 = "978-0-549-92090-8",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "Order Number AAI3338375.",
abstract = "Graphs express relationships among objects, such as
the radio connectivity among nodes in unmanned vehicle
swarms. Some applications may rank a swarm's nodes by
their relative importance, for example, using the
PageRank algorithm applied in certain search engines to
order query responses. The PageRank values of the nodes
correspond to a unique eigenvector that can be computed
using the power method, an iterative technique based on
matrix multiplication. The first result is a practical
lower bound on the PageRank algorithm's execution time
that is derived by applying assumptions to the PageRank
perturbation scaling value and the PageRank vector's
required numerical precision. The second result
establishes nodes contained in the same block of the
graph's coarsest equitable partition must have equal
PageRank values. The third result, the AverageRank
algorithm, ensures such nodes receive equal PageRank
values. The fourth result, the ProductRank algorithm,
reduces the time needed to compute the PageRank vector
by eliminating certain dot products in the power method
if the graph's coarsest equitable partition contains
blocks composed of multiple vertices. The fifth result,
the QuotientRank algorithm, uses the quotient matrix
induced by the coarsest equitable partition to further
decrease the time needed to obtain a swarm's PageRank
vector. \par
The practical lower bound on the PageRank algorithm's
execution time was previously only suggested using
experimental results. The proof establishing vertices
contained in the same block of the graph's coarsest
equitable partition have equal PageRank values is based
on relating dot products and Weisfeiler-Lehman
stabilization, a much different approach than applied
in an existing proof. The existing proof was also
extended to show the quotient matrix could be used to
reduce the PageRank algorithm's execution time.
However, its authors did not develop an algorithm or
analyze its execution time bounds. These results
motivate many avenues of future research related to
graph isomorphism and linear algebra.",
acknowledgement = ack-nhfb,
}
@InProceedings{Avrachenkov:2008:PBC,
author = "Konstantin Avrachenkov and Vladimir Dobrynin and Danil
Nemirovsky and Son Kim Pham and Elena Smirnova",
editor = "{ACM}",
booktitle = "Proceedings of the 31st Annual International ACM SIGIR
Conference on Research and Development in Information
Retrieval",
title = "{PageRank} based clustering of hypertext document
collections",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "873--874",
year = "2008",
DOI = "https://doi.org/10.1145/511446.511513",
ISBN = "1-60558-164-X",
ISBN-13 = "978-1-60558-164-4",
LCCN = "????",
bibdate = "Sat May 8 18:33:05 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Clustering hypertext document collection is an
important task in Information Retrieval. Most
clustering methods are based on document content and do
not take into account the hyper-text links. Here we
propose a novel PageRank based clustering (PRC)
algorithm which uses the hypertext structure. The PRC
algorithm produces graph partitioning with high
modularity and coverage. The comparison of the PRC
algorithm with two content based clustering algorithms
shows that there is a good match between PRC clustering
and content based clustering.",
acknowledgement = ack-nhfb,
keywords = "directed graphs; PageRank based clustering",
}
@Article{Avrachenkov:2008:SPA,
author = "Konstantin Avrachenkov and Nelly Litvak and Kim Son
Pham",
title = "A singular perturbation approach for choosing the
{PageRank} damping factor",
journal = j-INTERNET-MATH,
volume = "5",
number = "1--2",
pages = "47--69",
year = "2008",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
MRclass = "68R10 (05C82 68M11)",
MRnumber = "MR2560262",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/getRecord?id=euclid.im/1259158597",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@InProceedings{Bar-Yossef:2008:LAPa,
author = "Ziv Bar-Yossef and Li-Tal Mashiach",
editor = "{ACM}",
booktitle = "Proceedings of the 31st annual international ACM SIGIR
conference on Research and development in information
retrieval",
title = "Local approximation of {PageRank} and reverse
{PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "865--866",
year = "2008",
DOI = "https://doi.org/10.1145/1031171.1031248",
ISBN = "1-60558-164-X",
ISBN-13 = "978-1-60558-164-4",
LCCN = "????",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We consider the problem of approximating the PageRank
of a target node using only local information provided
by a link server. We prove that local approximation of
PageRank is feasible if and only if the graph has low
in-degree and admits fast PageRank convergence. While
natural graphs, such as the web graph, are abundant
with high in-degree nodes, making local PageRank
approximation too costly, we show that reverse natural
graphs tend to have low in degree while maintaining
fast PageRank convergence. It follows that calculating
Reverse PageRank locally is frequently more feasible
than computing PageRank locally. Finally, we
demonstrate the usefulness of Reverse PageRank in five
different applications.",
acknowledgement = ack-nhfb,
keywords = "local approximation; lower bounds; PageRank; reverse
PageRank",
}
@InProceedings{Bar-Yossef:2008:LAPb,
author = "Ziv Bar-Yossef and Li-Tal Mashiach",
editor = "{ACM}",
booktitle = "Conference on Information and Knowledge Management
Proceeding of the 17th ACM conference on Information
and knowledge management",
title = "Local approximation of {PageRank} and {Reverse
PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "279--288",
year = "2008",
DOI = "https://doi.org/10.1016/0890-5401(89)90067-9",
ISBN = "1-59593-991-1",
ISBN-13 = "978-1-59593-991-3",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We consider the problem of approximating the PageRank
of a target node using only local information provided
by a link server. This problem was originally studied
by Chen, Gan, and Suel (CIKM 2004), who presented an
algorithm for tackling it. We prove that local
approximation of PageRank, even to within modest
approximation factors, is infeasible in the worst-case,
as it requires probing the link server for $ \Omega $
(n) nodes, where n is the size of the graph. The
difficulty emanates from nodes of high in-degree and/or
from slow convergence of the PageRank random walk.
\par
We show that when the graph has bounded in-degree and
admits fast PageRank convergence, then local PageRank
approximation can be done using a small number of
queries. Unfortunately, natural graphs, such as the web
graph, are abundant with high in-degree nodes, making
this algorithm (or any other local approximation
algorithm) too costly. On the other hand, reverse
natural graphs tend to have low in-degree while
maintaining fast PageRank convergence. It follows that
calculating Reverse PageRank locally is frequently more
feasible than computing PageRank locally. \par
We demonstrate that Reverse PageRank is useful for
several applications, including computation of hub
scores for web pages, finding influencers in social
networks, obtaining good seeds for crawling, and
measurement of semantic relatedness between concepts in
a taxonomy.",
acknowledgement = ack-nhfb,
keywords = "local approximation; lower bounds; pagerank; reverse
pagerank",
}
@InProceedings{Bauckhage:2008:ITU,
author = "Christian Bauckhage",
editor = "Gerhard Rigoll",
booktitle = "Proceedings of the 30th DAGM Symposium on Pattern
Recognition",
title = "Image Tagging Using {PageRank} over Bipartite Graphs",
volume = "5096",
publisher = pub-SV,
address = pub-SV:adr,
pages = "426--435",
year = "2008",
DOI = "https://doi.org/10.1007/978-3-540-69321-5_43",
ISBN = "3-540-69320-3",
ISBN-13 = "978-3-540-69320-8",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "TA1650 .D35 2008",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "We consider the problem of automatic image tagging for
online services and explore a prototype-based approach
that applies ideas from manifold ranking. Since
algorithms for ranking on graphs or manifolds often
lack a way of dealing with out of sample data, they are
of limited use for pattern recognition. In this paper,
we therefore propose to consider diffusion processes
over bipartite graphs which allow for a dual treatment
of objects and features. As with Google's PageRank,
this leads to Markov processes over the prototypes. In
contrast to related methods, our model provides a
Bayesian interpretation of the transition matrix and
enables the ranking and consequently the classification
of unknown entities. By design, the method is tailored
to histogram features and we apply it to
histogram-based color image analysis. Experiments with
images downloaded from flickr.com illustrate object
localization in realistic scenes.",
acknowledgement = ack-nhfb,
}
@Article{Bini:2008:ESP,
author = "Dario A. Bini and Gianna M. {Del Corso} and Francesco
Romani",
title = "Evaluating scientific products by means of
citation-based models: a first analysis and
validation",
journal = j-ELECTRON-TRANS-NUMER-ANAL,
volume = "33",
pages = "1--16",
year = "2008\slash 2009",
CODEN = "????",
ISSN = "1068-9613 (print), 1097-4067 (electronic)",
ISSN-L = "1068-9613",
bibdate = "Mon Sep 6 12:28:30 MDT 2010",
bibsource = "http://etna.mcs.kent.edu/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://etna.mcs.kent.edu/vol.33.2008-2009/pp1-16.dir/pp1-16.pdf",
acknowledgement = ack-nhfb,
fjournal = "Electronic Transactions on Numerical Analysis",
}
@InProceedings{Boldi:2008:TPT,
author = "Paolo Boldi and Roberto Posenato and Massimo Santini
and Sebastiano Vigna",
title = "Traps and Pitfalls of Topic-Biased {PageRank}",
crossref = "Aiello:2008:AMW",
pages = "107--116",
year = "2008",
DOI = "https://doi.org/10.1007/978-3-540-78808-9_10",
ISBN = "3-540-78807-7",
ISBN-13 = "978-3-540-78807-2",
LCCN = "????",
MRclass = "68M10 68R10 68U35",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1142.68309",
abstract = "We discuss a number of issues in the definition,
computation and comparison of PageRank values that have
been addressed sparsely in the literature, often with
contradictory approaches. We study the difference
between weakly and strongly preferential PageRank,
which patch the dangling nodes with different
distributions, extending analytical formulae known for
the strongly preferential case, and corroborating our
results with experiments on a snapshot of 100 millions
of pages of the {\tt .uk} domain. The experiments show
that the two PageRank versions are poorly correlated,
and results about each one cannot be blindly applied to
the other; moreover, our computations highlight some
new concerns about the usage of exchange-based
correlation indices (such as Kendall's $ \tau $) on
approximated rankings.",
acknowledgement = ack-nhfb,
}
@Article{Brezinski:2008:REP,
author = "C. Brezinski and M. Redivo-Zaglia",
title = "Rational extrapolation for the {PageRank} vector",
journal = j-MATH-COMPUT,
volume = "77",
number = "263",
pages = "1585--1598",
month = jul,
year = "2008",
CODEN = "MCMPAF",
DOI = "https://doi.org/10.1090/S0025-5718-08-02086-3",
ISSN = "0025-5718 (print), 1088-6842 (electronic)",
ISSN-L = "0025-5718",
MRclass = "68U35 (65F15)",
MRnumber = "MR2398781 (2009d:68171)",
MRreviewer = "Stefano Serra Capizzano",
bibdate = "Tue Jul 8 06:24:30 MDT 2008",
bibsource = "http://www.ams.org/mcom/2008-77-263;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.ams.org/mcom/2008-77-263/S0025-5718-08-02086-3/home.html;
http://www.ams.org/mcom/2008-77-263/S0025-5718-08-02086-3/S0025-5718-08-02086-3.dvi;
http://www.ams.org/mcom/2008-77-263/S0025-5718-08-02086-3/S0025-5718-08-02086-3.pdf;
http://www.ams.org/mcom/2008-77-263/S0025-5718-08-02086-3/S0025-5718-08-02086-3.ps;
https://www.math.utah.edu/pub/tex/bib/mathcomp2000.bib",
acknowledgement = ack-nhfb,
fjournal = "Mathematics of Computation",
journal-URL = "http://www.ams.org/mcom/",
}
@InProceedings{Chebolu:2008:PRS,
author = "Prasad Chebolu and P{\'a}ll Melsted",
title = "{PageRank} and the random surfer model",
crossref = "ACM:2008:PNA",
pages = "1010--1018",
year = "2008",
DOI = "https://doi.org/10.1145/316188.316229",
MRclass = "68R10 (05C80 68P20 68U99)",
MRnumber = "MR2487672",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In recent years there has been considerable interest
in analyzing random graph models for the Web. We
consider two such models --- the Random Surfer model,
introduced by Blum et al. [7], and the PageRank-based
selection model, proposed by Pandurangan et al. [18].
It has been observed that search engines influence the
growth of the Web. The PageRank-based selection model
tries to capture the effect that these search engines
have on the growth of the Web by adding new links
according to Pagerank. The PageRank algorithm is used
in the Google search engine [1] for ranking search
results. \par
We show the equivalence of the two random graph models
and carry out the analysis in the Random Surfer model,
since it is easier to work with. We analyze the
expected in-degree of vertices and show that it follows
a powerlaw. We also analyze the expected PageRank of
vertices and show that it follows the same powerlaw as
the expected degree. \par
We show that in both models the expected degree and the
PageRank of the first vertex, the root of the graph,
follow the same powerlaw. However, the power undergoes
a phase-transition as we vary the parameter of the
model. This peculiar behavior of the root has not been
observed in previous analysis and simulations of the
two models.",
acknowledgement = ack-nhfb,
}
@Article{deKerchove:2008:MPO,
author = "Cristobald de Kerchove and Laure Ninove and Paul van
Dooren",
title = "Maximizing {PageRank} via outlinks",
journal = j-LINEAR-ALGEBRA-APPL,
volume = "429",
number = "5--6",
pages = "1254--1276",
day = "1",
month = sep,
year = "2008",
CODEN = "LAAPAW",
DOI = "https://doi.org/10.1016/j.laa.2008.01.023",
ISSN = "0024-3795 (print), 1873-1856 (electronic)",
ISSN-L = "0024-3795",
MRclass = "15A18 (15A51 15A57 60J10 68U35)",
MRnumber = "MR2433177 (2009e:15030)",
MRreviewer = "Thomas H. Foregger",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/00243795",
ZMnumber = "1147.68387",
acknowledgement = ack-nhfb,
fjournal = "Linear Algebra and its Applications",
journal-URL = "http://www.sciencedirect.com/science/journal/00243795",
}
@Article{DeSterck:2008:MAA,
author = "H. {De Sterck} and Thomas A. Manteuffel and Stephen F.
McCormick and Quoc Nguyen and John Ruge",
title = "Multilevel Adaptive Aggregation for {Markov} Chains,
with Application to {Web} Ranking",
journal = j-SIAM-J-SCI-COMP,
volume = "30",
number = "5",
pages = "2235--2262",
month = "????",
year = "2008",
CODEN = "SJOCE3",
DOI = "https://doi.org/10.1137/070685142",
ISSN = "1064-8275 (print), 1095-7197 (electronic)",
ISSN-L = "1064-8275",
bibdate = "Wed May 19 10:44:08 MDT 2010",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SISC/30/5;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "A multilevel adaptive aggregation method for
calculating the stationary probability vector of an
irreducible stochastic matrix is described. The method
is a special case of the adaptive smoothed aggregation
and adaptive algebraic multigrid methods for sparse
linear systems and is also closely related to certain
extensively studied iterative
aggregation/disaggregation methods for Markov chains.
In contrast to most existing approaches, our
aggregation process does not employ any explicit
advance knowledge of the topology of the Markov chain.
Instead, adaptive agglomeration is proposed that is
based on the strength of connection in a scaled problem
matrix, in which the columns of the original problem
matrix at each recursive fine level are scaled with the
current probability vector iterate at that level. The
strength of connection is determined as in the
algebraic multigrid method, and the aggregation process
is fully adaptive, with optimized aggregates chosen in
each step of the iteration and at all recursive levels.
The multilevel method is applied to a set of stochastic
matrices that provide models for web page ranking.
Numerical tests serve to illustrate for which types of
stochastic matrices the multilevel adaptive method may
provide significant speedup compared to standard
iterative methods. The tests also provide more insight
into why Google's PageRank model is a successful model
for determining a ranking of web pages.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Scientific Computing",
journal-URL = "http://epubs.siam.org/sisc",
}
@Article{Fiala:2008:PBN,
author = "Dalibor Fiala and Fran{\c{c}}ois Rousselot and Karel
Je{\v{z}}ek",
title = "{PageRank} for bibliographic networks",
journal = j-SCIENTOMETRICS,
volume = "76",
number = "1",
pages = "135--158",
month = may,
year = "2008",
CODEN = "SCNTDX",
DOI = "https://doi.org/10.1007/s11192-007-1908-4",
ISSN = "0138-9130 (print), 1588-2861 (electronic)",
ISSN-L = "0138-9130",
bibdate = "Tue Aug 11 16:41:10 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/article/10.1007/s11192-007-1908-4",
acknowledgement = ack-nhfb,
fjournal = "Scientometrics",
journal-URL = "http://link.springer.com/journal/11192",
}
@InProceedings{Fortunato:2008:APD,
author = "Santo Fortunato and Mari{\'a}n Bogu{\~n}{\'a} and
Alessandro Flammini and Filippo Menczer",
title = "Approximating {PageRank} from In-Degree",
crossref = "Aiello:2008:AMW",
pages = "59--71",
year = "2008",
DOI = "https://doi.org/10.1007/978-3-540-78808-9_6",
ISBN = "3-540-78807-7",
ISBN-13 = "978-3-540-78807-2",
LCCN = "????",
MRclass = "68M10 68U35 68P20",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1142.68311",
abstract = "PageRank is a key element in the success of search
engines, allowing to rank the most important hits in
the top screen of results. One key aspect that
distinguishes PageRank from other prestige measures
such as in-degree is its global nature. From the
information provider perspective, this makes it
difficult or impossible to predict how their pages will
be ranked. Consequently a market has emerged for the
optimization of search engine results. Here we study
the accuracy with which PageRank can be approximated by
in-degree, a local measure made freely available by
search engines. Theoretical and empirical analyses lead
to conclude that given the weak degree correlations in
the Web link graph, the approximation can be relatively
accurate, giving service and information providers an
effective new marketing tool.",
acknowledgement = ack-nhfb,
}
@InProceedings{Govan:2008:GGP,
author = "Anjela Y. Govan and Carl D. Meyer and Russell
Albright",
booktitle = "{Proceedings of the SAS Global Forum 2008: March
16--19, 2008, Henry B. Gonzalez Convention Center, San
Antonio, Texas}",
title = "Generalizing {Google}'s {PageRank} to rank national
football league teams",
publisher = pub-SAS,
address = pub-SAS:adr,
pages = "??--??",
year = "2008",
ISBN = "",
ISBN-13 = "",
LCCN = "",
bibdate = "Tue Aug 11 16:57:24 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "SAS paper 151-2008.",
URL = "http://www2.sas.com/proceedings/forum2008/151-2008.pdf",
acknowledgement = ack-nhfb,
book-URL = "http://www2.sas.com/proceedings/forum2008/TOC.html",
}
@InProceedings{Guo:2008:IBM,
author = "Chonghui Guo and Liang Zhang",
editor = "{IEEE}",
booktitle = "{WiCOM '08. 4th International Conference on (Online)
Wireless Communications, Networking and Mobile
Computing, Dalian, China, 12--17 October 2008}",
title = "An Improved {BA} Model Based on the {PageRank}
Algorithm",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "1--4",
year = "2008",
DOI = "https://doi.org/10.1109/WiCom.2008.2675",
ISBN = "1-4244-2107-1",
ISBN-13 = "978-1-4244-2107-7",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4680864",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4677908",
}
@InProceedings{Gupta:2008:FAT,
author = "Manish Gupta and Amit Pathak and Soumen Chakrabarti",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceeding of
the 17th international conference on World Wide Web",
title = "Fast algorithms for top-$k$ personalized {PageRank}
queries",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "1225--1226",
year = "2008",
DOI = "https://doi.org/10.1145/775152.775191",
ISBN = "1-60558-085-6",
ISBN-13 = "978-1-60558-085-2",
LCCN = "????",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In entity-relation (ER) graphs $ (V, E) $, nodes $V$
represent typed entities and edges $E$ represent typed
relations. For dynamic personalized PageRank queries,
nodes are ranked by their steady-state probabilities
obtained using the standard random surfer model. In
this work, we propose a framework to answer top-$k$
graph conductance queries. Our top-$k$ ranking
technique leads to a 4$ \times $ speedup, and overall,
our system executes queries 200-1600$ \times $ faster
than whole-graph PageRank. Some queries might contain
hard predicates i.e. predicates that must be satisfied
by the answer nodes. E.g. we may seek authoritative
papers on public key cryptography, but only those
written during 1997. We extend our system to handle
hard predicates. Our system achieves these substantial
query speedups while consuming only 10--20\% of the
space taken by a regular text index.",
acknowledgement = ack-nhfb,
keywords = "HubRank; node-deletion; pagerank; personalized;
top-$k$",
}
@Article{Hristidis:2008:ABK,
author = "Vagelis Hristidis and Heasoo Hwang and Yannis
Papakonstantinou",
title = "Authority-based keyword search in databases",
journal = j-TODS,
volume = "33",
number = "1",
pages = "1:1--1:??",
month = mar,
year = "2008",
CODEN = "ATDSD3",
DOI = "https://doi.org/10.1145/1331904.1331905",
ISSN = "0362-5915 (print), 1557-4644 (electronic)",
ISSN-L = "0362-5915",
bibdate = "Thu Jun 12 16:37:49 MDT 2008",
bibsource = "http://www.acm.org/pubs/contents/journals/tods/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/tods.bib",
abstract = "Our system applies authority-based ranking to keyword
search in databases modeled as labeled graphs. Three
ranking factors are used: the relevance to the query,
the specificity and the importance of the result. All
factors are handled using authority-flow techniques
that exploit the link-structure of the data graph, in
contrast to traditional Information Retrieval. We
address the performance challenges in computing the
authority flows in databases by using precomputation
and exploiting the database schema if present. We
conducted user surveys and performance experiments on
multiple real and synthetic datasets, to assess the
semantic meaningfulness and performance of our
system.",
acknowledgement = ack-nhfb,
articleno = "1",
fjournal = "ACM Transactions on Database Systems",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J777",
keywords = "Authority flow; PageRank; quality experiments;
ranking; specificity",
}
@Article{Ipsen:2008:PCS,
author = "Ilse C. F. Ipsen and Teresa M. Selee",
title = "{PageRank} Computation, with Special Attention to
Dangling Nodes",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "29",
number = "4",
pages = "1281--1296",
month = "????",
year = "2008",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/060664331",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
bibdate = "Tue May 18 22:32:22 MDT 2010",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toclist/SIMAX/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
}
@InProceedings{Ishii:2008:DRAa,
author = "H. Ishii and R. Tempo",
booktitle = "{CDC 2008: 47th IEEE Conference on Decision and
Control}",
title = "A distributed randomized approach for the {PageRank}
computation: {Part 1}",
crossref = "IEEE:2008:ICD",
pages = "3523--3528",
year = "2008",
DOI = "https://doi.org/10.1109/CDC.2008.4739020",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4739020",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721212",
}
@InProceedings{Ishii:2008:DRAb,
author = "H. Ishii and R. Tempo",
title = "A distributed randomized approach for the {PageRank}
computation: {Part 2}",
crossref = "IEEE:2008:ICD",
pages = "3529--3534",
year = "2008",
DOI = "https://doi.org/10.1109/CDC.2008.4739022",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4739022",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721212",
}
@InProceedings{Jing:2008:PPI,
author = "Yushi Jing and Shumeet Baluja",
editor = "{ACM}",
booktitle = "International World Wide Web Conference Proceeding of
the 17th international conference on World Wide Web",
title = "{PageRank} for product image search",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "307--316",
year = "2008",
DOI = "https://doi.org/10.1023/B:VISI.0000013087.49260.fb",
ISBN = "1-60558-085-6",
ISBN-13 = "978-1-60558-085-2",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In this paper, we cast the image-ranking problem into
the task of identifying 'authority' nodes on an
inferred visual similarity graph and propose an
algorithm to analyze the visual link structure that can
be created among a group of images. Through an
iterative procedure based on the PageRank computation,
a numerical weight is assigned to each image; this
measures its relative importance to the other images
being considered. The incorporation of visual signals
in this process differs from the majority of
large-scale commercial-search engines in use today.
Commercial search-engines often solely rely on the text
clues of the pages in which images are embedded to rank
images, and often entirely ignore the content of the
images themselves as a ranking signal. To quantify the
performance of our approach in a real-world system, we
conducted a series of experiments based on the task of
retrieving images for 2000 of the most popular products
queries. Our experimental results show significant
improvement, in terms of user satisfaction and
relevancy, in comparison to the most recent Google
Image Search results.",
acknowledgement = ack-nhfb,
keywords = "graph theory; pagerank; visual similarity",
}
@Article{Jing:2008:VAP,
author = "Yushi Jing and S. Baluja",
title = "{VisualRank}: Applying {PageRank} to Large-Scale Image
Search",
journal = j-IEEE-TRANS-PATT-ANAL-MACH-INTEL,
volume = "30",
number = "11",
pages = "1877--1890",
month = nov,
year = "2008",
CODEN = "ITPIDJ",
DOI = "https://doi.org/10.1109/TPAMI.2008.121",
ISSN = "0162-8828",
ISSN-L = "0162-8828",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4522561",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34",
fjournal = "IEEE Transactions on Pattern Analysis and Machine
Intelligence",
journal-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34",
}
@InProceedings{Kale:2008:DRE,
author = "M. Kale and P. S. Thilagam",
booktitle = "{ICCSIT '08: International Conference on Computer
Science and Information Technology}",
title = "{DYNA-RANK}: Efficient Calculation and Updation of
{PageRank}",
crossref = "IEEE:2008:PIC",
pages = "808--812",
year = "2008",
DOI = "https://doi.org/10.1109/ICCSIT.2008.118",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4624979",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4624812",
}
@Article{Kaplan:2008:BRB,
author = "Daniel T. Kaplan",
title = "Book Review: {{\booktitle{Google}'s PageRank and
Beyond: The Science of Search Engine Rankings}, by Amy
N. Langville; Carl D. Meyer}",
journal = j-AMER-MATH-MONTHLY,
volume = "115",
number = "8",
pages = "765--768",
month = oct,
year = "2008",
CODEN = "AMMYAE",
ISSN = "0002-9890 (print), 1930-0972 (electronic)",
ISSN-L = "0002-9890",
bibdate = "Mon Jan 30 12:00:31 MST 2012",
bibsource = "http://www.jstor.org/journals/00029890.html;
http://www.jstor.org/stable/i27642579;
https://www.math.utah.edu/pub/tex/bib/amermathmonthly2000.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.jstor.org/stable/27642602",
acknowledgement = ack-nhfb,
fjournal = "American Mathematical Monthly",
journal-URL = "https://www.jstor.org/journals/00029890.htm",
}
@TechReport{Leung:2008:PNM,
author = "Ye Du and James Leung and Yaoyun Shi",
title = "{PerturbationRank}: A Non-monotone Ranking Algorithm",
type = "Technology Report",
institution = "University of Michigan",
address = "Ann Arbor, MI, USA",
pages = "10",
year = "2008",
bibdate = "Tue Aug 11 16:39:02 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://web.eecs.umich.edu/~shiyy/mypapers/DLS08.pdf",
acknowledgement = ack-nhfb,
}
@InProceedings{Li:2008:APA,
author = "Fagui Li and Tong Yi",
editor = "{IEEE}",
booktitle = "{PACIIA '08: Pacific-Asia Workshop on Computational
Intelligence and Industrial Application (2008)}",
title = "Apply {PageRank} Algorithm to Measuring Relationship's
Complexity",
volume = "1",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "914--917",
year = "2008",
DOI = "https://doi.org/10.1109/PACIIA.2008.309",
ISBN = "0-7695-3490-2",
ISBN-13 = "978-0-7695-3490-9",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4756692",
abstract = "Software measurement can help software developers
analyze reliability, maintainability and complexity of
systems. Till now, researchers have proposed lots of
metrics for UML class diagrams range from cohesion to
couple. However very little work is involved in
measuring weights of relationships. This paper
describes how to measure weights of relationships
objectively and mechanically, in which famous PageRank
algorithm in web structure mining is used. Finally, a
small but realistic example is illustrated.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4756503",
keywords = "pagerank algorithm; software measurement; unified
modeling language",
}
@Article{Lin:2008:PHR,
author = "Jimmy Lin",
title = "{PageRank} without hyperlinks: Reranking with {PubMed}
related article networks for biomedical text
retrieval",
journal = j-BMC-BIOINFORMATICS,
volume = "9",
pages = "270--271",
year = "2008",
CODEN = "BBMIC4",
DOI = "https://doi.org/10.1186/1471-2105-9-270",
ISSN = "1471-2105",
bibdate = "Fri Jun 3 10:03:23 MDT 2011",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.biomedcentral.com/1471-2105/9/270;
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2442104/",
abstract = "Graph analysis algorithms such as PageRank and HITS
have been successful in Web environments because they
are able to extract important inter-document
relationships from manually-created hyperlinks. We
consider the application of these techniques to
biomedical text retrieval. In the current PubMed search
interface, a MEDLINE citation is connected to a number
of related citations, which are in turn connected to
other citations. Thus, a MEDLINE record represents a
node in a vast content-similarity network. This article
explores the hypothesis that these networks can be
exploited for text retrieval, in the same manner as
hyperlink graphs on the Web.",
acknowledgement = ack-nhfb,
ajournal = "BMC Bioinf.",
fjournal = "BMC Bioinformatics",
journal-URL = "http://www.biomedcentral.com/bmcbioinformatics/",
keywords = "BioMed Central (BMC)",
}
@InProceedings{Litvak:2008:PRB,
author = "Nelly Litvak and Werner R. W. Scheinhardt and Yana
Volkovich",
title = "Probabilistic relation between in-degree and
{PageRank}",
crossref = "Aiello:2008:AMW",
pages = "72--83",
year = "2008",
DOI = "https://doi.org/10.1007/978-3-540-78808-9_7",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
MRclass = "68M10 (05C90 37A50 68P20)",
MRnumber = "MR2473494 (2010c:68014)",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
ZMnumber = "1142.68314",
abstract = "This paper presents a novel stochastic model that
explains the relation between power laws of In-Degree
and PageRank. PageRank is a popularity measure designed
by Google to rank Web pages. We model the relation
between PageRank and In-Degree through a stochastic
equation, which is inspired by the original definition
of PageRank. Using the theory of regular variation and
Tauberian theorems, we prove that the tail
distributions of PageRank and In-Degree differ only by
a multiplicative constant, for which we derive a
closed-form expression. Our analytical results are in
good agreement with Web data.",
acknowledgement = ack-nhfb,
keywords = "Algorithms; Experimentation; In-Degree; PageRank;
Power law; Regular variation; Stochastic equation;
Theory; Verification; Web measurement",
}
@InProceedings{Liu:2008:BLW,
author = "Y. Liu and B. Gao and T.-Y. Liu and Y. Zhang and Z. Ma
and S. He and H. Li",
editor = "Sung Hyon Myaeng and Douglas W. Oard and Fabrizio
Sebastiani and T. S. (Tat-Seng) Chua and Mun-Kew
Leong",
booktitle = "{ACM SIGIR 2008: proceedings of the thirty-first
annual International ACM SIGIR Conference on Research
and Development in Information Retrieval: July 20--24,
2008, Singapore}",
title = "{BrowseRank}: Letting web users vote for page
importance",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "451--458",
year = "2008",
DOI = "https://doi.org/10.1145/1390334.1390412",
ISBN = "1-60558-164-X",
ISBN-13 = "978-1-60558-164-4",
LCCN = "QA76.9.D3",
bibdate = "Tue Aug 11 17:22:35 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://dl.acm.org/citation.cfm?id=1390334",
acknowledgement = ack-nhfb,
book-URL = "http://www.sigir2008.org/papers.html",
bookpages = "xxviii + 906",
}
@InProceedings{Liu:2008:PPB,
author = "Yong Liu and Xiaolei Wang and Jin Zhang and Hongbo
Xu",
editor = "{IEEE}",
booktitle = "{WSCS '08: IEEE International Workshop on Semantic
Computing and Systems (2008)}",
title = "Personalized {PageRank} Based Multi-document
Summarization",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "169--173",
year = "2008",
DOI = "https://doi.org/10.1109/WSCS.2008.32",
ISBN = "0-7695-3316-7",
ISBN-13 = "978-0-7695-3316-2",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4570834",
abstract = "This paper presents a novel multi-document
summarization approach based on Personalized PageRank
(PPRSum). In this algorithm, we uniformly integrate
various kinds of information in the corpus. At first,
we train a salience model of sentence global features
based on Na{\"\i}ve Bayes Model. Secondly, we generate
a relevance model for each corpus utilizing the query
of it. Then, we compute the personalized prior
probability for each sentence in the corpus utilizing
the salience model and the relevance model both. With
the help of personalized prior probability, a
Personalized PageRank ranking process is performed
depending on the relationships among all sentences in
the corpus. Additionally, the redundancy penalty is
imposed on each sentence. The summary is produced by
choosing the sentences with both high query-focused
information richness and high information novelty.
Experiments on DUC2007 are performed and the ROUGE
evaluation results show that PPRSum ranks between the
1st and the 2nd systems on DUC2007 main task.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4570797",
keywords = "Personalized PageRank; Na{\"\i}ve Bayes model;
personalized prior probability",
}
@Article{Ma:2008:BPC,
author = "Nan Ma and Jiancheng Guan and Yi Zhao",
title = "Bringing {PageRank} to the citation analysis",
journal = "Information Processing and Management: an
International Journal",
volume = "44",
number = "2",
pages = "800--810",
month = mar,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1145/324133.324140",
ISSN = "????",
bibdate = "Sat May 8 18:33:04 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "The paper attempts to provide an alternative method
for measuring the importance of scientific papers based
on the Google's PageRank. The method is a meaningful
extension of the common integer counting of citations
and is then experimented for bringing PageRank to the
citation analysis in a large citation network. It
offers a more integrated picture of the publications'
influence in a specific field. We firstly calculate the
PageRanks of scientific papers. The distributional
characteristics and comparison with the traditionally
used number of citations are then analyzed in detail.
Furthermore, the PageRank is implemented in the
evaluation of research influence for several countries
in the field of Biochemistry and Molecular Biology
during the time period of 2000-2005. Finally, some
advantages of bringing PageRank to the citation
analysis are concluded.",
acknowledgement = ack-nhfb,
keywords = "citation analysis; citation network; internal
citations; PageRank",
}
@InProceedings{McGettrick:2008:FAP,
author = "S. McGettrick and D. Geraghty and C. McElroy",
editor = "Udo Kebschull and Marco Platzner and J{\"u}rgen
Teich",
booktitle = "{FPL 2008: International Conference on
Field-Programmable Logic and Applications: Heidelberg,
Germany, September 8--10, 2008}",
title = "An {FPGA} architecture for the {PageRank} eigenvector
problem",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "523--526",
year = "2008",
DOI = "https://doi.org/10.1109/FPL.2008.4629999",
ISBN = "1-4244-1961-1, 1-4244-1960-3 (set)",
ISBN-13 = "978-1-4244-1961-6, 978-1-4244-1960-9 (set)",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "IEEE catalog number CFP08623.",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4629999",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4625340",
}
@InProceedings{McGettrick:2008:TFS,
author = "S{\'e}amas McGettrick and Dermot Geraghty and
Ciar{\'a}n McElroy",
editor = "Christian Bischof and others",
booktitle = "Parallel computing: Architectures, algorithms and
applications. Selected papers based on the
presentations at the international parallel computing
conference (ParCo 2007), Aachen, Germany, September
4--7, 2007",
title = "Towards an {FPGA} solver for the {PageRank}
eigenvector problem",
volume = "15",
publisher = pub-IOS,
address = pub-IOS:adr,
pages = "793--800",
year = "2008",
ISBN = "1-58603-796-X",
ISBN-13 = "978-1-58603-796-3",
LCCN = "????",
MRclass = "68M10 65F30 65Y10 65Y20",
bibdate = "Thu May 06 11:31:36 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = "Advances in Parallel Computing",
ZMnumber = "1160.68317",
acknowledgement = ack-nhfb,
}
@Article{Pan:2008:APA,
author = "Hao Pan and Long-Yuan Tan",
title = "Adaptive {PageRank} algorithm search strategy for
specific topics",
journal = "J. Comput. Appl.",
volume = "28",
number = "9",
pages = "2192--2194",
year = "2008",
CODEN = "????",
ISSN = "????",
MRclass = "68M11 68M10 68P10",
bibdate = "Thu May 06 11:29:26 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1180.68039",
acknowledgement = ack-nhfb,
language = "Chinese",
}
@Article{Parreira:2008:JAP,
author = "Josiane Xavier Parreira and Carlos Castillo and Debora
Donato and Sebastian Michel and Gerhard Weikum",
title = "The {Juxtaposed} approximate {PageRank} method for
robust {PageRank} approximation in a peer-to-peer web
search network",
journal = j-VLDB-J,
volume = "17",
number = "2",
pages = "291--313",
month = mar,
year = "2008",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-007-0057-y",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "We present Juxtaposed approximate PageRank (JXP), a
distributed algorithm for computing PageRank-style
authority scores of Web pages on a peer-to-peer (P2P)
network. Unlike previous algorithms, JXP allows peers
to have overlapping content and requires no a priori
knowledge of other peers' content. Our algorithm
combines locally computed authority scores with
information obtained from other peers by means of
random meetings among the peers in the network. This
computation is based on a Markov-chain state-lumping
technique, and iteratively approximates global
authority scores. The algorithm scales with the number
of peers in the network and we show that the JXP scores
converge to the true PageRank scores that one would
obtain with a centralized algorithm. Finally, we show
how to deal with misbehaving peers by extending JXP
with a reputation model.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
keywords = "link analysis; Markov chain aggregation; peer-to-peer
systems; social reputation; Web graph",
}
@InProceedings{Pathak:2008:IDD,
author = "Amit Pathak and Soumen Chakrabarti and Manish Gupta",
editor = "{IEEE}",
booktitle = "{ICDE 2008: IEEE 24th International Conference on Data
Engineering}",
title = "Index Design for Dynamic Personalized {PageRank}",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "1489--1491",
year = "2008",
DOI = "https://doi.org/10.1109/ICDE.2008.4497599",
ISBN = "1-4244-1836-4",
ISBN-13 = "978-1-4244-1836-7",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4497599",
abstract = "Personalized page rank, related to random walks with
restarts and conductance in resistive networks, is a
frequent search paradigm for graph-structured
databases. While efficient batch algorithms exist for
static whole-graph page rank, interactive query-time
personalized page rank has proved more challenging.
Here we describe how to select and build indices for a
popular class of page rank algorithms, so as to provide
real-time personalized page rank and smoothly trade off
between index size, preprocessing time, and query
speed. We achieve this by developing a precise, yet
efficiently estimated performance model for
personalized page rank query execution. We use this
model in conjunction with a query workload in a
cost-benefit type index optimizer. On millions of
queries from CiteSeer and its data graphs with 74--320
thousand nodes, our algorithm runs 50-400 $ \times $
faster than whole-graph page rank, the gap growing with
graph size. Index size is 10--20\% of a text index.
Ranking accuracy is above 94\%.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4492792",
}
@InProceedings{Sarma:2008:EPG,
author = "Atish Das Sarma and Sreenivas Gollapudi and Rina
Panigrahy",
title = "Estimating {PageRank} on graph streams",
crossref = "Lenzerini:2008:PTS",
pages = "69--78",
year = "2008",
DOI = "https://doi.org/10.1145/1376916.1376928",
bibdate = "Fri Jun 20 14:17:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/pods.bib",
abstract = "This study focuses on computations on large graphs
(e.g., the web-graph) where the edges of the graph are
presented as a stream. The objective in the streaming
model is to use small amount of memory (preferably
sub-linear in the number of nodes $n$) and a few
passes.\par
In the streaming model, we show how to perform several
graph computations including estimating the probability
distribution after a random walk of length $l$, mixing
time, and the conductance. We estimate the mixing time
$M$ of a random walk in $ \tilde {O}(n \alpha + M
\alpha \sqrt {n} + \sqrt {M n / \alpha })$ space and $
\tilde {O}(\sqrt {M} \alpha)$ passes. Furthermore, the
relation between mixing time and conductance gives us
an estimate for the conductance of the graph. By
applying our algorithm for computing probability
distribution on the Web-graph, we can estimate the
PageRank $p$ of any node up to an additive error of $
\sqrt {\epsilon } p$ in $ \tilde {O}(\sqrt {M} /
\alpha)$ passes and $ \tilde {O}(\min (n \alpha + 1 /
\epsilon \sqrt {M} / \alpha + 1 / \epsilon M \alpha,
\alpha n \sqrt {M} \alpha + 1 / \epsilon \sqrt {M} /
\alpha))$ space, for any $ \alpha \in (0, 1]$. In
particular, for $ \epsilon = M / n$, by setting $
\alpha = M^{-1 / 2}$, we can compute the approximate
PageRank values in $ \tilde {O}(n M^{-1 / 4})$ space
and $ \tilde {O}(M^{3 / 4})$ passes. In comparison, a
standard implementation of the PageRank algorithm will
take $ O(n)$ space and $ O(M)$ passes.",
acknowledgement = ack-nhfb,
keywords = "graph conductance; mixing time; PageRank; random walk;
streaming algorithms",
}
@Article{Sidi:2008:VEM,
author = "Avram Sidi",
title = "Vector extrapolation methods with applications to
solution of large systems of equations and to
{PageRank} computations",
journal = j-COMPUT-MATH-APPL,
volume = "56",
number = "1",
pages = "1--24",
month = jul,
year = "2008",
CODEN = "CMAPDK",
DOI = "https://doi.org/10.1016/j.camwa.2007.11.027",
ISSN = "0898-1221 (print), 1873-7668 (electronic)",
ISSN-L = "0898-1221",
MRclass = "65F50 (65F10 65F15)",
MRnumber = "MR2427680 (2009j:65109)",
MRreviewer = "Cristina Tablino Possio",
bibdate = "Sat May 8 18:33:11 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1145.65312",
abstract = "An important problem that arises in different areas of
science and engineering is that of computing the limits
of sequences of vectors {x'n}, where x'n@?C^N with N
very large. Such sequences arise, for example, in the
solution of systems of linear or nonlinear equations by
fixed-point iterative methods, and lim'n'->'~x'n are
simply the required solutions. In most cases of
interest, however, these sequences converge to their
limits extremely slowly. One practical way to make the
sequences {x'n} converge more quickly is to apply to
them vector extrapolation methods. In this work, we
review two polynomial-type vector extrapolation methods
that have proved to be very efficient convergence
accelerators; namely, the minimal polynomial
extrapolation (MPE) and the reduced rank extrapolation
(RRE). We discuss the derivation of these methods,
describe the most accurate and stable algorithms for
their implementation along with the effective modes of
usage in solving systems of equations, nonlinear as
well as linear, and present their convergence and
stability theory. We also discuss their close
connection with the method of Arnoldi and with GMRES,
two well-known Krylov subspace methods for linear
systems. We show that they can be used very effectively
to obtain the dominant eigenvectors of large sparse
matrices when the corresponding eigenvalues are known,
and provide the relevant theory as well. One such
problem is that of computing the PageRank of the Google
matrix, which we discuss in detail. In addition, we
show that a recent extrapolation method of Kamvar et
al. that was proposed for computing the PageRank is
very closely related to MPE. We present a
generalization of the method of Kamvar et al. along
with a very economical algorithm for this
generalization. We also provide the missing convergence
theory for it.",
acknowledgement = ack-nhfb,
fjournal = "Computers \& Mathematics with Applications. An
International Journal",
keywords = "Eigenvalue problems; Google matrix; Iterative methods;
Krylov subspace methods; Large sparse systems of
equations; Minimal polynomial extrapolation; PageRank
computations; Power iterations; Reduced rank
extrapolation; Singular linear systems; Stochastic
matrices; Vector extrapolation methods",
}
@Article{Stringer:2008:EJR,
author = "M. J. Stringer and M. Sales-Pardo and L. S. {Nunes
Amaral}",
title = "Effectiveness of journal ranking schemes as a tool for
locating information",
journal = j-PLOS-ONE,
volume = "3",
number = "2",
pages = "e1683:1--e1683:8",
day = "27",
month = feb,
year = "2008",
CODEN = "POLNCL",
DOI = "https://doi.org/10.1371/journal.pone.0001683",
ISSN = "1932-6203",
bibdate = "Fri Mar 11 16:17:22 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0001683",
acknowledgement = ack-nhfb,
fjournal = "PLoS One",
journal-URL = "http://www.plosone.org/",
}
@InProceedings{Su:2008:ERR,
author = "Ja-Hwung Su and Bo-Wen Wang and Vincent S. Tseng",
editor = "{IEEE}",
booktitle = "{WI-IAT '08: IEEE\slash WIC\slash ACM International
Conference on Web Intelligence and Intelligent Agent
Technology (2008)}",
title = "Effective Ranking and Recommendation on {Web} Page
Retrieval by Integrating Association Mining and
{PageRank}",
volume = "3",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "455--458",
year = "2008",
DOI = "https://doi.org/10.1109/WIIAT.2008.49",
ISBN = "0-7695-3496-1",
ISBN-13 = "978-0-7695-3496-1",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4740820",
abstract = "Nowadays, the well-known search engines, such as
Google, Yahoo, MSN, etc, have provided the users with
good search results based on special search strategies.
However there still exist some problems unsolved for
traditional search engines, including: (1) the gap
between user's intention and searched results is not
easy to narrow down under the global search space, and
(2) user's interested pages hidden in the local website
are not associated with the search results. To deal
with such problems, in this paper, we propose a novel
approach for personalized page ranking and
recommendation by integrating association mining and
PageRank so as to meet user's search goals. Moreover,
by mining the users' browsing behaviors, we can
successfully bridge the gap between global search
results and local preferences. The effectiveness of our
proposed approach was verified through experimental
evaluations.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4740404",
}
@InProceedings{Tripathy:2008:WMA,
author = "Animesh Tripathy and Prashanta K. Patra",
editor = "{IEEE}",
booktitle = "{APSCC '08: IEEE Asia-Pacific Services Computing
Conference (2008)}",
title = "A {Web} Mining Architectural Model of Distributed
Crawler for {Internet} Searches Using {PageRank}
Algorithm",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "513--518",
year = "2008",
DOI = "https://doi.org/10.1109/APSCC.2008.259",
ISBN = "0-7695-3473-2",
ISBN-13 = "978-0-7695-3473-2",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4780726",
abstract = "As the World Wide Web is growing rapidly and data in
the present day scenario is stored in a distributed
manner. The need to develop a search engine based
architectural model for people to search through the
Web. Broad web search engines as well as many more
specialized search tools rely on web crawlers to
acquire large collections of pages for indexing and
analysis. The crawler is an important module of a web
search engine. The quality of a crawler directly
affects the searching quality of such web search
engines. Such a web crawler may interact with millions
of hosts over a period of weeks or months, and thus
issues of robustness, flexibility, and manageability
are of major importance. Given some URLs, the crawler
should retrieve the web pages of those URLs, parse the
HTML files, add new URLs into its queue and go back to
the first phase of this cycle. The crawler also can
retrieve some other information from the HTML files as
it is parsing them to get the new URLs. In this paper,
we describe the design of a web crawler that uses
PageRank algorithm for distributed searches and can be
run on a network of workstations. The crawler scales to
several hundred pages per second, is resilient against
system crashes and other events, and can be adapted to
various crawling applications. We present web mining
architecture of the system and describe efficient
techniques for achieving high performance.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4780614",
keywords = "Crawler; Data Mining; PageRank; Web Mining",
}
@MastersThesis{Tudisco:2008:MAN,
author = "F. Tudisco",
title = "Metodi analitico numerici per il problema del ranking
delle pagine web. ({Italian}) [{Numerical} analytic
method for the problem of ranking {Web} pages]",
type = "Bachelor thesis",
school = "Dipartimento di Matematica, Universit{\`a} degli studi
di Roma ``Tor Vergata''",
address = "Rome, Italy",
year = "2008",
bibdate = "Wed Nov 30 08:15:21 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
language = "Italian",
}
@Article{Wang:2008:DSZ,
author = "Xuanhui Wang and Tao Tao and Jian-Tao Sun and Azadeh
Shakery and Chengxiang Zhai",
title = "{DirichletRank}: {Solving} the zero-one gap problem of
{PageRank}",
journal = j-TOIS,
volume = "26",
number = "2",
pages = "10:1--10:??",
month = mar,
year = "2008",
CODEN = "ATISET",
DOI = "https://doi.org/10.1145/1344411.1344416",
ISSN = "1046-8188",
ISSN-L = "0734-2047",
bibdate = "Thu Jun 12 16:52:34 MDT 2008",
bibsource = "http://www.acm.org/pubs/contents/journals/tois/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/tois.bib",
abstract = "Link-based ranking algorithms are among the most
important techniques to improve web search. In
particular, the PageRank algorithm has been
successfully used in the Google search engine and has
been attracting much attention recently. However, we
find that PageRank has a ``zero-one gap'' problem
which, to the best of our knowledge, has not been
addressed in any previous work. This problem can be
potentially exploited to spam PageRank results and make
the state-of-the-art link-based antispamming techniques
ineffective. The zero-one gap problem arises as a
result of the current ad hoc way of computing
transition probabilities in the random surfing model.
We therefore propose a novel DirichletRank algorithm
which calculates these probabilities using Bayesian
estimation with a Dirichlet prior. DirichletRank is a
variant of PageRank, but does not have the problem of
zero-one gap and can be analytically shown
substantially more resistant to some link spams than
PageRank. Experiment results on TREC data show that
DirichletRank can achieve better retrieval accuracy
than PageRank due to its more reasonable allocation of
transition probabilities. More importantly, experiments
on the TREC dataset and another real web dataset from
the Webgraph project show that, compared with the
original PageRank, DirichletRank is more stable under
link perturbation and is significantly more robust
against both manually identified web spams and several
simulated link spams. DirichletRank can be computed as
efficiently as PageRank, and thus is scalable to
large-scale web applications.",
acknowledgement = ack-nhfb,
articleno = "10",
fjournal = "ACM Transactions on Information Systems",
keywords = "DirichletRank; link analysis; PageRank; spamming;
zero-one gap",
}
@InProceedings{Wang:2008:KIS,
author = "Jinghua Wang and Jianyi Liu and Cong Wang and Ping
Zhang",
booktitle = "{ICNSC 2008: IEEE International Conference on
Networking, Sensing and Control}",
title = "Keyword Indexing System with {HowNet} and {PageRank}",
crossref = "IEEE:2008:PII",
pages = "389--393",
year = "2008",
DOI = "https://doi.org/10.1109/ICNSC.2008.4525246",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4525246",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4489617",
}
@InProceedings{Wang:2008:RDR,
author = "Jue Wang and Jian Peng and Daping Zhang",
editor = "{IEEE}",
booktitle = "CSSE '08: Proceedings of the 2008 International
Conference on Computer Science and Software
Engineering",
title = "Research on Dynamic Reputation Management Model Based
on {PageRank}",
volume = "3",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "814--817",
year = "2008",
DOI = "https://doi.org/10.1109/CSSE.2008.927",
ISBN = "0-7695-3336-1",
ISBN-13 = "978-0-7695-3336-0",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4722467",
abstract = "For the purpose of developing a usable trust
relationship between the resource providers (hosts) and
the resource consumers (users) in an open computing
environment and providing a unified management of the
reputation degree of the resource provides and users, a
dynamic reputation management model based on Google
PageRank (DRMPR) is proposed. The DRMPR system can
achieve self-study from a large amount of data and
feedback, and with the system obtaining a plenty of
resources, the judgment is more accurate. At the end of
the paper, an experimental project has been built to
demonstrate that the DRMPR can provide a unified
management of the reputation degree of the resource
provides and users accurately.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721667",
keywords = "feedback; PageRank; reputation; trust",
}
@Article{Wu:2008:CJC,
author = "Gang Wu and Yimin Wei",
title = "Comments on: {``Jordan canonical form of the Google
matrix: a potential contribution to the PageRank
computation'' [SIAM J. Matrix Anal. Appl. {\bf 27}
(2005), no. 2, 305--312; MR2179674] by S.
Serra-Capizzano}",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "30",
number = "1",
pages = "364--374",
year = "2008",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/070682204",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
MRclass = "65F15 (15A57 65C40 65F10)",
MRnumber = "MR2399585 (2009c:65093)",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "See \cite{Serra-Capizzano:2005:JCF}.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
}
@Article{Wu:2008:EJC,
author = "Gang Wu",
title = "Eigenvalues and {Jordan} canonical form of a
successively rank-one updated complex matrix with
applications to {Google}'s {PageRank} problem",
journal = j-J-COMPUT-APPL-MATH,
volume = "216",
number = "2",
pages = "364--370",
month = jun,
year = "2008",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2007.05.015",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
MRclass = "15A18 (65F15 68U35); 15A21 65F15 15A18 15A57 68P10",
MRnumber = "MR2412913 (2009a:15037)",
MRreviewer = "Ross A. Lippert",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
ZMnumber = "1148.15007",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
keywords = "65C40; 65F10; 65F15; Generalized Google matrix; Google
matrix; Jordan canonical form; Pagerank; Successively
rank-one updated matrix",
}
@InProceedings{Yang:2008:APT,
author = "Shenggang Yang and Jianmin Zhao and Xueyan Zhang and
Limei Zhao",
editor = "Elvis Wai Chung Leung and others",
booktitle = "{Advances in Blended Learning: Second Workshop on
Blended Learning, WBL 2008, Jinhua, China, August
20--22, 2008. Revised Selected Papers}",
title = "Application of {PageRank} Technique in Collaborative
Learning",
publisher = pub-SV,
address = pub-SV:adr,
pages = "102--109",
year = "2008",
DOI = "https://doi.org/10.1007/978-3-540-89962-4_11",
ISBN = "3-540-89962-6",
ISBN-13 = "978-3-540-89962-4",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "With the rapid development in web 2.0, lots of realm
communities provide free platforms for users to enrich
their knowledge through online communication, sharing
and socializing without boundaries. As an on-line
system may interact with thousands of users, it is
almost impossible for the field experts or teachers to
give instant help manually, which is not only
inefficient, but also human laborious. To cope with it,
an E-learning community should construct an efficiency
knowledge acquiring mechanism. To assure this
mechanism, this research applies PageRank-based
mechanism to rank knowledge items synthetically. The
system appraises the knowledge items provided by
learners based on their rank, other users remarks and
most importantly teachers' and realm experts' remarks,
thus picks out the KIs to the knowledge base. In return
the users' grade will be upgraded or degraded by their
KIs. Learners are served with knowledge that best
matches their needs and encouraged by each other. Thus
this study sets up an aspiring and aggressive
collaborative learning environment. Experiments results
have shown that the developed system.",
acknowledgement = ack-nhfb,
keywords = "Collaborative/cooperative learning; fairness gene;
knowledge acquiring; PageRank",
}
@InProceedings{Zhang:2008:NRA,
author = "Liyan Zhang and Chunping Li",
editor = "Wayne Wobcke and Mengjie Zhang",
booktitle = "Proceedings of the 21st Australasian Joint Conference
on Artificial Intelligence: Advances in Artificial
Intelligence",
title = "A Novel Recommending Algorithm Based on Topical
{PageRank}",
volume = "5360",
publisher = pub-SV,
address = pub-SV:adr,
pages = "447--453",
year = "2008",
DOI = "https://doi.org/10.1007/978-3-540-89378-3_45",
ISBN = "3-540-89377-6",
ISBN-13 = "978-3-540-89377-6",
LCCN = "Q334 .A97 2008",
bibdate = "Sat May 8 18:33:07 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNAI,
abstract = "In this paper, we propose a Topical PageRank based
algorithm for recommender systems, which ranks products
by analyzing previous user-item relationships, and
recommends top-rank items to potentially interested
users. In order to rank all the items for each
particular user, we attempt to establish a correlation
graph among items, and implement ranking process with
our algorithm. We evaluate our algorithm on MovieLens
dataset and empirical experiments demonstrate that it
outperforms other state-of-the-art recommending
algorithms.",
acknowledgement = ack-nhfb,
}
@InProceedings{Zhang:2008:RAW,
author = "Yong Zhang and Long-bin Xiao and Bin Fan",
booktitle = "{FSKD '08: Fifth International Conference on Fuzzy
Systems and Knowledge Discovery (2008)}",
title = "The Research about {Web} Page Ranking Based on the
{A-PageRank} and the {Extended VSM}",
crossref = "Ma:2008:FFI",
volume = "4",
pages = "223--227",
year = "2008",
DOI = "https://doi.org/10.1109/FSKD.2008.267",
ISBN = "0-7695-3305-1",
ISBN-13 = "978-0-7695-3305-6",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4666388",
abstract = "The web page rank algorithm is always regarded as the
core of the search engine. Firstly, this article
analyzes the traditional and classical rank algorithms
briefly. Then, it proposes a new rank algorithm, which
is called A-PageRank. In this algorithm, the PageRank
value of the source page is distributed to its Link-out
pages according to the topic similarity. Lastly, a new
method which uses both the similarity and divergence to
weigh the match degree between one web page and one
user query is adopted in order to increase the
precision and recall rate of the search engine.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4665920",
keywords = "A-PageRank; anchor text; PageRank; PFT; VSM",
}
@InProceedings{Zhang:2008:TPB,
author = "Liyan Zhang and Kai Zhang and Chunping Li",
editor = "{ACM}",
booktitle = "Annual ACM Conference on Research and Development in
Information Retrieval Proceedings of the 31st annual
international ACM SIGIR conference on Research and
development in information retrieval",
title = "A topical {PageRank} based algorithm for recommender
systems",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "713--714",
year = "2008",
DOI = "https://doi.org/10.1145/1148170.1148189",
ISBN = "1-60558-164-X",
ISBN-13 = "978-1-60558-164-4",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In this paper, we propose a Topical PageRank based
algorithm for recommender systems, which aim to rank
products by analyzing previous user-item relationships,
and recommend top-rank items to potentially interested
users. We evaluate our algorithm on MovieLens dataset
and empirical experiments demonstrate that it
outperforms other state-of-the-art recommending
algorithms.",
acknowledgement = ack-nhfb,
keywords = "recommender system; topical PageRank",
}
@InProceedings{Agirre:2009:PPW,
author = "Eneko Agirre and Aitor Soroa",
editor = "Alex Lascarides and Claire Gardent and Joakim Nivre",
booktitle = "{Proceedings of the 12th Conference of the European
Chapter of the Association for Computational
Linguistics: 30 March--3 April 2009, Megaron Athens
International Conference Centre, Athens, Greece}",
title = "Personalizing {PageRank} for word sense
disambiguation",
publisher = "Association for Computational Linguistics",
address = "Morristown, NJ, USA",
pages = "33--41",
year = "2009",
DOI = "https://doi.org/10.1109/ICSC.2007.107",
ISBN = "1-932432-16-7",
ISBN-13 = "978-1-932432-16-9",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In this paper we propose a new graph-based method that
uses the knowledge in a LKB (based on WordNet) in order
to perform unsupervised Word Sense Disambiguation. Our
algorithm uses the full graph of the LKB efficiently,
performing better than previous approaches in English
all-words datasets. We also show that the algorithm can
be easily ported to other languages with good results,
with the only requirement of having a WordNet. In
addition, we make an analysis of the performance of the
algorithm, showing that it is efficient and that it
could be tuned to be faster.",
acknowledgement = ack-nhfb,
}
@InProceedings{Alam:2009:FPC,
author = "Md. Hijbul Alam and Jongwoo Ha and Sangkeun Lee",
editor = "Xiaofang Zhou and others",
booktitle = "Proceedings of the 14th International Conference on
Database Systems for Advanced Applications",
title = "Fractional {PageRank} Crawler: Prioritizing {URLs}
Efficiently for Crawling Important Pages Early",
volume = "5463",
publisher = pub-SV,
address = pub-SV:adr,
pages = "590--594",
year = "2009",
DOI = "https://doi.org/10.1007/978-3-642-00887-0_52",
ISBN = "3-642-00886-0",
ISBN-13 = "978-3-642-00886-3",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.9.D3 I58 2009",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "Crawling important pages early is a well studied
problem. However, the availability of different types
of framework for publishing web content greatly
increases the number of web pages. Therefore, the
crawler should be fast enough to prioritize and
download the important pages. As the importance of a
page is not known before or during its download, the
crawler needs a great deal of time to approximate the
importance to prioritize the download of the web pages.
In this research, we propose Fractional PageRank
crawlers that prioritize the downloaded pages for the
purpose of discovering important URLs early during the
crawl. Our experiments demonstrate that they improve
the running time dramatically while crawling the
important pages early.",
acknowledgement = ack-nhfb,
bookpages = "xix + 797",
}
@Article{Bar-Yossef:2009:DCD,
author = "Ziv Bar-Yossef and Idit Keidar and Uri Schonfeld",
title = "Do not crawl in the {DUST}: {Different URLs with
Similar Text}",
journal = j-TWEB,
volume = "3",
number = "1",
pages = "3:1--3:??",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1145/1462148.1462151",
ISSN = "1559-1131 (print), 1559-114X (electronic)",
ISSN-L = "1559-1131",
bibdate = "Fri Apr 24 18:18:15 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/tweb.bib",
abstract = "We consider the problem of DUST: Different URLs with
Similar Text. Such duplicate URLs are prevalent in Web
sites, as Web server software often uses aliases and
redirections, and dynamically generates the same page
from various different URL requests. We present a novel
algorithm, {\em DustBuster}, for uncovering DUST; that
is, for discovering rules that transform a given URL to
others that are likely to have similar content.
DustBuster mines DUST effectively from previous crawl
logs or Web server logs, {\em without\/} examining page
contents. Verifying these rules via sampling requires
fetching few actual Web pages. Search engines can
benefit from information about DUST to increase the
effectiveness of crawling, reduce indexing overhead,
and improve the quality of popularity statistics such
as PageRank.",
acknowledgement = ack-nhfb,
articleno = "3",
fjournal = "ACM Transactions on the Web (TWEB)",
keywords = "antialiasing; crawling; duplicate detection; Search
engines; URL normalization",
}
@Article{Boldi:2009:PFD,
author = "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
title = "{PageRank}: {Functional} dependencies",
journal = j-TOIS,
volume = "27",
number = "4",
pages = "19:1--19:??",
month = nov,
year = "2009",
CODEN = "ATISET",
DOI = "https://doi.org/10.1145/1062745.1062826",
ISSN = "1046-8188",
ISSN-L = "0734-2047",
bibdate = "Mon Mar 15 12:37:02 MDT 2010",
bibsource = "http://www.acm.org/pubs/contents/journals/tois/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
articleno = "19",
fjournal = "ACM Transactions on Information Systems",
keywords = "damping factor; PageRank; power method",
}
@InProceedings{Chen:2009:IPA,
author = "Xiaoyun Chen and Baojun Gao and Ping Wen",
editor = "Xin Li and Wenbin Hu and others",
booktitle = "{Proceedings, 2009 International Conference on
Information Engineering and Computer Science: ICIECS
2009, Wuhan China 19--20 December 2009}",
title = "An Improved {PageRank} Algorithm Based on Latent
Semantic Model",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "1--4",
year = "2009",
DOI = "https://doi.org/10.1109/ICIECS.2009.5364637",
ISBN = "1-4244-4994-4",
ISBN-13 = "978-1-4244-4994-1",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "IEEE catalog number CFP0990H.",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5364637",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5362513",
}
@InProceedings{Chen:2009:SNE,
author = "Wei Chen and Shang-Hua Teng and Yajun Wang and Yuan
Zhou",
title = "On the $ \alpha $-sensitivity of {Nash} equilibria in
{PageRank}-based network reputation games",
crossref = "Deng:2009:FAT",
volume = "5598",
pages = "63--73",
year = "2009",
DOI = "https://doi.org/10.1007/978-3-642-02270-8_9",
ISBN = "3-642-02269-3",
ISBN-13 = "978-3-642-02269-2",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "????",
MRclass = "68Wxx",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
ZMnumber = "05578464",
abstract = "Web search engines use link-based reputation systems
(e.g. PageRank) to measure the importance of web pages,
giving rise to the strategic manipulations of
hyperlinks by spammers and others to boost their web
pages' reputation scores. Hopcroft and Sheldon [10]
study this phenomenon by proposing a network formation
game in which nodes strategically select their outgoing
links in order to maximize their PageRank scores. They
pose an open question in [10] asking whether all Nash
equilibria in the PageRank game are insensitive to the
restart probability $ \alpha $ of the PageRank
algorithm. They show that a positive answer to the
question would imply that all Nash equilibria in the
PageRank game must satisfy some strong algebraic
symmetry, a property rarely satisfied by real web
graphs. In this paper, we give a negative answer to
this open question. We present a family of graphs that
are Nash equilibria in the PageRank game only for
certain choices of $ \alpha $.",
acknowledgement = ack-nhfb,
}
@InProceedings{Chung:2009:LGP,
author = "Fan Chung",
title = "A Local Graph Partitioning Algorithm Using Heat Kernel
{PageRank}",
crossref = "Avrachenkov:2009:AMW",
pages = "62--75",
year = "2009",
DOI = "https://doi.org/10.1007/978-3-540-95995-3_6",
ISBN = "3-540-95994-7",
ISBN-13 = "978-3-540-95994-6",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "????",
MRclass = "68M10",
bibdate = "Sat May 8 18:33:04 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
ZMnumber = "05505865",
abstract = "We give an improved local partitioning algorithm using
heat kernel pagerank, a modified version of PageRank.
For a subset S with Cheeger ratio (or conductance) h,
we show that there are at least a quarter of the
vertices in S that can serve as seeds for heat kernel
pagerank which lead to local cuts with Cheeger ratio at
most $ O(\sqrt {h}) $, improving the previously bound
by a factor of $ \sqrt {log|S|} $.",
acknowledgement = ack-nhfb,
}
@Misc{Cutts:2009:PS,
author = "Matt Cutts",
title = "{PageRank} sculpting",
howpublished = "Gadgets, Google, and SEO blog.",
year = "2009",
bibdate = "Tue Aug 11 16:35:56 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.mattcutts.com/blog/pagerank-sculpting/",
acknowledgement = ack-nhfb,
}
@InProceedings{Deng:2009:GEF,
author = "Kaiying Deng and Tieli Sun and Jingwei Deng",
editor = "{IEEE}",
booktitle = "{FSKD '09: Sixth International Conference on Fuzzy
Systems and Knowledge Discovery (2009)}",
title = "The General Extrapolation Formula for Acceleration
{PageRank} Computations",
volume = "7",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "590--594",
year = "2009",
DOI = "https://doi.org/10.1109/FSKD.2009.112",
ISBN = "0-7695-3735-9",
ISBN-13 = "978-0-7695-3735-1",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5360078",
abstract = "Based on the foundation work for PageRank
computations, we further derive the general formula for
accelerating PageRank computations. And we also discuss
the method for generating high dimension stochastic
matrix, being characterized the Web graph. Numerical
results confirm the effectiveness of the theoretical
analysis and numerical algorithms.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5358480",
keywords = "Hyperlink Analysis; Information Retrieval; PageRank",
}
@Article{Ding:2009:PRA,
author = "Ying Ding and Erjia Yan and Arthur Frazho and James
Caverlee",
title = "{PageRank} for ranking authors in co-citation
networks",
journal = "Journal of the American Society for Information
Science and Technology",
volume = "60",
number = "11",
pages = "2229--2243",
month = nov,
year = "2009",
CODEN = "JASIEF",
DOI = "https://doi.org/10.1145/1013367.1013519",
ISSN = "1532-2882 (print), 1532-2890 (electronic)",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "This paper studies how varied damping factors in the
PageRank algorithm influence the ranking of authors and
proposes weighted PageRank algorithms. We selected the
108 most highly cited authors in the information
retrieval (IR) area from the 1970s to 2008 to form the
author co-citation network. We calculated the ranks of
these 108 authors based on PageRank with the damping
factor ranging from 0.05 to 0.95. In order to test the
relationship between different measures, we compared
PageRank and weighted PageRank results with the
citation ranking, h-index, and centrality measures. We
found that in our author co-citation network, citation
rank is highly correlated with PageRank with different
damping factors and also with different weighted
PageRank algorithms; citation rank and PageRank are not
significantly correlated with centrality measures; and
h-index rank does not significantly correlate with
centrality measures but does significantly correlate
with other measures. The key factors that have impact
on the PageRank of authors in the author co-citation
network are being co-cited with important authors.",
acknowledgement = ack-nhfb,
ajournal = "J. Am. Soc. Inf. Sci. Technol.",
fjournal = "Journal of the American Society for Information
Science and Technology",
keywords = "authors; citation analysis; co-citation networks;
ranking; weighting",
}
@InProceedings{Gao:2009:KNM,
author = "Lianxiong Gao and Jianping Wu and Liu Rui",
booktitle = "{CCDC '09: Chinese Control and Decision Conference
(2009)}",
title = "Key nodes mining in transport networks based in
{PageRank} algorithm",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "4413--4416",
year = "2009",
DOI = "https://doi.org/10.1109/CCDC.2009.5192339",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5192339",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5174536",
}
@InProceedings{Imran:2009:ERP,
author = "Naveed Imran and Jingen Liu and Jiebo Luo and Mubarak
Shah",
editor = "{ACM}",
booktitle = "International Multimedia Conference Proceedings of the
seventeen ACM international conference on Multimedia",
title = "Event recognition from photo collections via
PageRank",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "621--624",
year = "2009",
DOI = "https://doi.org/10.1109/ICCV.2005.20",
ISBN = "1-60558-608-0",
ISBN-13 = "978-1-60558-608-3",
LCCN = "????",
bibdate = "Sat May 8 18:33:08 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We propose a method of mining most informative
features for the event recognition from photo
collections. Our goal is to classify different event
categories based on the visual content of a group of
photos that constitute the event. Such photo groups are
typical in a personal photo collection of different
events. Visual features are extracted from the images,
yet the features from individual images are often noisy
and not all of them represent the distinguishing
characteristics of an event. We employ the PageRank
technique to mine the most informative features from
the images that belong to the same event. Subsequently,
we classify different event categories using the
multiple images of the same event because we argue that
they are more informative about the content of an event
rather than any single image. We compare our proposed
approach with the standard bag of features method (BOF)
and observe considerable improvements in recognition
accuracy.",
acknowledgement = ack-nhfb,
keywords = "CBIR; event category recognition; pagerank",
}
@InProceedings{Ishii:2009:DPC,
author = "Hideaki Ishii and Roberto Tempo",
editor = "{IEEE}",
booktitle = "{ACC '09: American Control Conference (2009)}",
title = "Distributed {PageRank} computation with link
failures",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "1976--1981",
year = "2009",
DOI = "https://doi.org/10.1109/ACC.2009.5160351",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5160351",
abstract = "The Google search engine employs the so-called
PageRank algorithm for ranking the search results. This
algorithm quantifies the importance of each web page
based on the link structure of the web. In this paper,
we continue our recent work on distributed randomized
computation of PageRank, where the pages locally
determine their values by communicating with linked
pages. In particular, we propose a distributed
randomized algorithm with limited information, where
only part of the linked pages is required to be
contacted. This is useful to enhance flexibility and
robustness in computation and communication.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5089257",
keywords = "distributed computation; link failures; multi-agent
consensus; pagerank algorithm; randomization;
stochastic matrices",
}
@InProceedings{Ishii:2009:DRP,
author = "H. Ishii and R. Tempo and Er-Wei Bai and F. Dabbene",
booktitle = "{CDC\slash CCC 2009: Proceedings of the 48th IEEE
Conference on Decision and Control [held jointly with
the 2009 28th Chinese Control Conference]}",
title = "Distributed randomized {PageRank} computation based on
web aggregation",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "3026--3031",
year = "2009",
DOI = "https://doi.org/10.1109/CDC.2009.5399514",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5399514",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695",
}
@InProceedings{Ishii:2009:FLS,
author = "H. Ishii and R. Tempo",
booktitle = "{CDC\slash CCC 2009: Proceedings of the 48th IEEE
Conference on Decision and Control [held jointly with
the 2009 28th Chinese Control Conference]}",
title = "Fragile link structure in {PageRank} computation",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "121--126",
year = "2009",
DOI = "https://doi.org/10.1109/CDC.2009.5399501",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5399501",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695",
}
@InProceedings{Jager:2009:PSH,
author = "Douglas V. Jager and Jeremy T. Bradley",
editor = "Leif Azzopardi and others",
booktitle = "{Advances in information retrieval theory: second
International Conference on the Theory of Information
Retrieval, ICTIR 2009, Cambridge, UK, September 10--12,
2009: proceedings}",
title = "{PageRank}: Splitting Homogeneous Singular Linear
Systems of Index One",
volume = "5766",
publisher = pub-SV,
address = pub-SV:adr,
pages = "17--28",
year = "2009",
DOI = "https://doi.org/10.1007/978-3-642-04417-5_3",
ISBN = "3-642-04416-6",
ISBN-13 = "978-3-642-04416-8",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.9.D3 I55887 2009",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "The PageRank algorithm is used today within web
information retrieval to provide a content-neutral
ranking metric over web pages. It employs power method
iterations to solve for the steady-state vector of a
DTMC. The defining one-step probability transition
matrix of this DTMC is derived from the hyperlink
structure of the web and a model of web surfing
behaviour which accounts for user bookmarks and
memorised URLs. \par
In this paper we look to provide a more accessible,
more broadly applicable explanation than has been given
in the literature of how to make PageRank calculation
more tractable through removal of the dangling-page
matrix. This allows web pages without outgoing links to
be removed before we employ power method iterations. It
also allows decomposition of the problem according to
irreducible subcomponents of the original transition
matrix. Our explanation also covers a PageRank
extension to accommodate TrustRank. In setting out our
alternative explanation, we introduce and apply a
general linear algebraic theorem which allows us to map
homogeneous singular linear systems of index one to
inhomogeneous non-singular linear systems with a shared
solution vector. As an aside, we show in this paper
that irreducibility is not required for PageRank to be
well-defined.",
acknowledgement = ack-nhfb,
}
@InProceedings{Jin:2009:APA,
author = "Ying Jin and Jing Zhang and Pengfei Ma and Weiping Hao
and Shutong Luo and Zepeng Li",
editor = "{IEEE}",
booktitle = "{COMPSAC '09: 33rd Annual IEEE International Computer
Software and Applications Conference, 2009}",
title = "Applying {PageRank} Algorithm in Requirement Concern
Impact Analysis",
volume = "1",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "361--366",
year = "2009",
DOI = "https://doi.org/10.1109/COMPSAC.2009.55",
ISBN = "0-7695-3726-X",
ISBN-13 = "978-0-7695-3726-9",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5254238",
abstract = "As an important part of requirement management,
managing requirement change plays a key role in
controlling project schedule and costs at early stage.
Effective requirement impact analysis would give proper
assessment on the effect of certain requirement changes
on the whole system, and provide useful information for
making trade-off decisions on future system design and
implementation. In this paper a quantitative approach
to concern impact analysis at requirement level has
been proposed with the application of PageRank
algorithm, which is a successful link based web page
sorting algorithm. At first, separation of concerns is
applied during deriving formal requirement
specification from textual requirement statements.
Next, concerns are specified and concern relationship
graph is established. Finally, PageRank algorithm is
utilized on concern relationship graph for assessing
the impact of concern changes. Our approach has been
applied to hallway section in Light Control System and
validation of analysis result has been stated.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5254044",
keywords = "concern impact analysis; concern relationship graph;
PageRank algorithm",
}
@Article{Kaul:2009:RBW,
author = "Rohit Kaul and Yeogirl Yun and Seong-Gon Kim",
title = "Ranking billions of {Web} pages using diodes",
journal = j-CACM,
volume = "52",
number = "8",
pages = "132--136",
month = aug,
year = "2009",
CODEN = "CACMA2",
DOI = "https://doi.org/10.1145/1536616.1536649",
ISSN = "0001-0782 (print), 1557-7317 (electronic)",
ISSN-L = "0001-0782",
bibdate = "Wed Sep 2 16:54:35 MDT 2009",
bibsource = "http://www.acm.org/pubs/contents/journals/cacm/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.math.utah.edu/pub/tex/bib/cacm2000.bib",
abstract = "Introduction\par
Because of the web's rapid growth and lack of central
organization, Internet search engines play a vital role
in assisting the users of the Web in retrieving
relevant information out of the tens of billions of
documents available. With millions of dollars of
potential revenue at stake, commercial Web sites
compete fiercely to be placed prominently within the
first page returned by a search engine. As a result,
search engine optimizers (SEOs) developed various forms
of search engine spamming (or spamdexing) techniques to
artificially inflate the rankings of Web pages.
Link-based ranking algorithms, such as Google's
PageRank, have been largely effective against most
conventional spamming techniques.\par
However, PageRank has three fundamental flaws that,
when exploited aggressively, can be proven to be its
Achilles' heel: First, PageRank gives a minimum
guaranteed score to every page on the Web; second, it
rewards all incoming links as valid endorsements; and
third, it imposes no penalty for making links to
low-quality pages. SEOs can take advantage of these
shortcomings to the extreme by employing an Artificial
Web, a collection of an extremely large number of
computer-generated Web pages containing many links to
only a few target pages. Each page of the Artificial
Web collects the minimum PageRank and feeds it back to
the target pages. Although the individual endorsements
are small, the flaws of PageRank make it possible for
an Artificial Web to accumulate sizable PageRank values
for the target pages. The SEOs can even download a
substantial portion of the real Web and modify only the
destinations of the hyperlinks, thus circumventing any
detection algorithms based on the quality or the size
of pages. As the size of an Artificial Web can be
comparable to that of the real Web, SEOs can seriously
compromise the objectivity of the results that PageRank
provides. Although some statistical measures can be
employed to identify specific attributes associated
with an Artificial Web and filter them out of search
results, it is far more desirable to develop a new
ranking model that is free of such exploits to begin
with.",
acknowledgement = ack-nhfb,
fjournal = "Communications of the ACM",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J79",
}
@TechReport{Kolda:2009:GBG,
author = "Tamara G. Kolda and Michel J. Procopio",
title = "Generalized {BadRank} with Graduated Trust",
type = "Technical Report",
number = "SAND2009-6670",
institution = "Sandia National Laboratories",
address = "Albuquerque, NM, USA",
pages = "27",
month = oct,
year = "2009",
bibdate = "Tue Aug 11 17:14:02 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.ca.sandia.gov/~tgkolda/pubs/bibtgkfiles/SAND2009-6670%20BadRank.pdf",
acknowledgement = ack-nhfb,
}
@InProceedings{Lianxiong:2009:KNM,
author = "Gao Lianxiong and Wu Jianping and Liu Rui",
editor = "{IEEE}",
booktitle = "Proceedings of the 21st annual international
conference on Chinese control and decision conference",
title = "Key nodes mining in transport networks based on
{PageRank} algorithm",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "4449--4452",
year = "2009",
DOI = "https://doi.org/10.1137/S0036144503424786",
ISBN = "1-4244-2722-3",
ISBN-13 = "978-1-4244-2722-2",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Transport networks display the features of complex
networks, in which the vertices importance measurement
is crucial. After analyzing some classic importance
measurements and the characteristics of transport
networks, NodeRank, a new method based on PageRank
algorithm, is proposed in this paper to measure the
importance of vertices in transportation network. Then
the constraint equation is deduced and the existence
and uniqueness of solutions are presented. The solving
algorithm is described and its convergence is analyzed.
Finally, we present a case applying our method to
mining key nodes in a real-world transport network.",
acknowledgement = ack-nhfb,
keywords = "complex network; key nodes mining; pagerank algorithm;
transport network",
}
@Article{Lin:2009:CPL,
author = "Yiqin Lin and Xinghua Shi and Yimin Wei",
title = "On computing {PageRank} via lumping the {Google}
matrix",
journal = j-J-COMPUT-APPL-MATH,
volume = "224",
number = "2",
pages = "702--708",
month = feb,
year = "2009",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2008.06.003",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
MRclass = "65F15",
MRnumber = "MR2492903 (2009k:65071)",
MRreviewer = "David Scott Watkins",
bibdate = "Sat May 8 18:33:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Computing Google's PageRank via lumping the Google
matrix was recently analyzed in [I. C. F. Ipsen, T. M.
Selee, PageRank computation, with special attention to
dangling nodes, SIAM J. Matrix Anal. Appl. 29 (2007)
1281--1296]. It was shown that all of the dangling
nodes can be lumped into a single node and the PageRank
could be obtained by applying the power method to the
reduced matrix. Furthermore, the stochastic reduced
matrix had the same nonzero eigenvalues as the full
Google matrix and the power method applied to the
reduced matrix had the same convergence rate as that of
the power method applied to the full matrix. Therefore,
a large amount of operations could be saved for
computing the full PageRank vector. In this note, we
show that the reduced matrix obtained by lumping the
dangling nodes can be further reduced by lumping a
class of nondangling nodes, called weakly nondangling
nodes, to another single node, and the further reduced
matrix is also stochastic with the same nonzero
eigenvalues as the Google matrix.",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
keywords = "65B99; 65F10; 65F15; 65F50; Dangling node; Google
matrix; Lumping; PageRank; Power method; Weakly
nondangling node",
}
@InProceedings{Ling:2009:IPW,
author = "Zhang Ling and Qin Zheng",
editor = "{IEEE}",
booktitle = "ICISE Proceedings of the 2009 First IEEE International
Conference on Information Science and Engineering",
title = "The Improved {PageRank} in {Web} Crawler",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1889--1892",
year = "2009",
DOI = "https://doi.org/10.1109/ICISE.2009.1220",
ISBN = "0-7695-3887-8",
ISBN-13 = "978-0-7695-3887-7",
LCCN = "????",
bibdate = "Sat May 8 18:33:04 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Pagerank is an algorithm for rating web pages. It
introduces the relationship of citation in academic
papers to evaluate the web page's authority. It gives
the same weight to all edges and ignores the relevancy
of web pages to the topic, resulting in a problem of
topic-drift. On the analysis of several pagerank
algorithms, an improved pagerank based upon thematic
segments is proposed. In this algorithm, a web page is
divided into several blocks by Html document's
structure and the most weight is given to linkages in
the block that is most relevant to given topic.
Moreover, the visited outlinks are regarded as feedback
to modify blocks' relevancy The experiment on Web
crawler shows that the new algorithm has some effect on
resolving the problem of topic-drift.",
acknowledgement = ack-nhfb,
}
@InProceedings{Litvak:2009:CTD,
author = "Nelly Litvak and Werner Scheinhardt and Yana Volkovich
and Bert Zwart",
title = "Characterization of Tail Dependence for In-Degree and
{PageRank}",
crossref = "Avrachenkov:2009:AMW",
pages = "90--103",
year = "2009",
DOI = "https://doi.org/10.1007/978-3-540-95995-3_8",
ISBN = "3-540-95994-7",
ISBN-13 = "978-3-540-95994-6",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "????",
bibdate = "Sat May 8 18:33:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
series = ser-LNCS,
abstract = "The dependencies between power law parameters such as
in-degree and PageRank, can be characterized by the
so-called angular measure, a notion used in extreme
value theory to describe the dependency between very
large values of coordinates of a random vector. Basing
on an analytical stochastic model, we argue that the
angular measure for in-degree and personalized PageRank
is concentrated in two points. This corresponds to the
two main factors for high ranking: large in-degree and
a high rank of one of the ancestors. Furthermore, we
can formally establish the relative importance of these
two factors.",
acknowledgement = ack-nhfb,
keywords = "Multivariate extremes; PageRank; Power law graphs;
Regular variation",
}
@InProceedings{Liu:2009:ERE,
author = "Yaqing Liu and Rong Chen and Hong Yang",
booktitle = "{ICIECS 2009: International Conference on Information
Engineering and Computer Science}",
title = "Entity-Relation Extraction for {Chinese} Based on
Pattern Evolution and {PageRank}",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "1--4",
year = "2009",
DOI = "https://doi.org/10.1109/ICIECS.2009.5364487",
ISBN = "1-4244-4994-4",
ISBN-13 = "978-1-4244-4994-1",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5364487",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5362513",
}
@Book{Lowe:2009:GSS,
author = "Janet Lowe",
title = "{Google} speaks: secrets of the world's greatest
billionaire entrepreneurs, {Sergey Brin} and {Larry
Page}",
publisher = pub-WILEY,
address = pub-WILEY:adr,
pages = "xiii + 315",
year = "2009",
ISBN = "0-470-50122-7 (e-book), 0-470-50124-3 (e-book: Adobe
Digital Editions), 0-470-50123-5 (e-book: Mobipocket
Reader), 0-470-39854-X (cloth)",
ISBN-13 = "978-0-470-50122-1 (e-book), 978-0-470-50124-5 (e-book:
Adobe Digital Editions), 978-0-470-50123-8 (e-book:
Mobipocket Reader), 978-0-470-39854-8 (cloth)",
LCCN = "QA76.2.A2 L69 2009eb",
bibdate = "Fri Jun 3 09:52:48 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
abstract = "An up-close look at the people and philosophies behind
one of the most important new companies of our time,
Google Speaks is an engaging and informative look at
one of the most important companies of the twenty-first
century. It reveals the amazing story behind Google, a
company that in less than 15 years has become a global
household name and, in the process, created a new model
for corporate responsibility and employee relations.
Lowe explores the values that drive Google's founders
and discusses how they have created a culture that
fosters creativity and fun, while at the same time,
keeping Google at the forefront of technology through
large, relentless R and D investments and imaginative
partnerships with organizations such as NASA. This book
also addresses controversies surrounding Google, such
as copyright infringement, antitrust concerns, and
personal privacy.",
acknowledgement = ack-nhfb,
subject = "Brin, Sergey; Page, Larry; Computer programmers;
United States; Biography; Businesspeople; Internet
programming; Google; Web search engines",
subject-dates = "1973--; 1973--",
tableofcontents = "Introduction \\
The Google guys: Sergey Brin; Larry Page; The power of
partnership; Networking at its best; Burning man \\
Adult supervision: The collective wisdom of Silicon
Valley; He's been the rock: they've been the rockets; A
man of influence; Climbing a different kind of mountain
\\
In the beginning: The ultimate search engine; Not
inventing, but improving upon; Look around you for
inspiration; How search works; Platform power; Open
platform \\
Google by any other name: A blessed blunder; From noun
to verb; Playing with the name; The Google logo; The
Google doodle; Google zeitgeist \\
A company is born: Yahoo! drew the map; The requisite
garage; The venture capitalists; The elusive business
plan; Investing in wild ideas; Good ideas put to good
use; Dealing with dark matter; Aversion to advertising;
Advertising that delivers results; Two ways to
advertise: AdWords and AdSense; Extending the Google
reach; The science of advertising; Google didn't
advertise itself - at first; Birth of the Google
economy \\
Going public: ``We're different''; The Dutch auction;
The Playboy interview; Ten years later \\
The vision: Make it useful; Make it big; Make it fun;
Don't do evil; Make it free \\
Google culture: New management style; Ten things Google
has found to be true; Riding the long tail; 20 percent
projects; Perpetual beta; Fabled workplace; An
alternative point of view; Googleplex; Google in
Ireland; Top ten reasons to work at Google; The battle
for brainpower; Guarding the secrets \\
Google grows up: Conflicts and controversy: Click
fraud; Avoiding - or not avoiding - pornography;
Privacy issue; Advertising products; Gmail; Street
view; Can they snoop - and will they tell?; Hello,
human rights; The great Chinese firewall; Principles of
freedom; Copyright infringement; The authors' revolt;
The game-changing settlement; Lawsuits everywhere;
Google gets an airplane; Google gets a satellite \\
Good citizen Google: Google.org: the philanthropic
part; Google and the environment; Renewable energy less
than coal; Geothermal power; Energy from the sea;
Energy-efficient Googleplex \\
Google's future: Artificial intelligence; Onward to Web
3.0; Cloud computing; YouTube; The Google phone; White
spaces \\
The dominant power in the industry?: Google Microsoft,
and the Internet civil war; The battle of Yahoo!; Gates
on Google \\
Conclusion: Lessons from Larry and Sergey; The traits
of those who change the world \\
Timeline \\
Glossary",
}
@InProceedings{Mataoui:2009:EPA,
author = "M. Mataoui and M. Boughanem and M. Mezghiche",
booktitle = "{ICADIWT '09: Second International Conference on the
Applications of Digital Information and Web
Technologies (2009)}",
title = "Experiments on {PageRank} algorithm in the {XML}
information retrieval context",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "393--398",
year = "2009",
DOI = "https://doi.org/10.1109/ICADIWT.2009.5273944",
ISBN = "1-4244-4456-X",
ISBN-13 = "978-1-4244-4456-4",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5273944",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5254891",
}
@Misc{Meng:2009:CBS,
author = "X. Meng",
title = "Computing {BookRank} via Social Cataloging",
howpublished = "Web slides for CADS 2010 conference.",
pages = "33",
day = "22",
month = feb,
year = "2009",
bibdate = "Tue Aug 11 17:25:15 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://cads.stanford.edu/projects/presentations/2009visit/bookrank.pdf",
acknowledgement = ack-nhfb,
}
@InProceedings{Nazin:2009:ARA,
author = "A. Nazin and B. Polyak",
booktitle = "{CDC\slash CCC 2009: Proceedings of the 48th IEEE
Conference on Decision and Control [held jointly with
the 2009 28th Chinese Control Conference]}",
title = "Adaptive randomized algorithm for finding eigenvector
of stochastic matrix with application to {PageRank}",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "127--132",
year = "2009",
DOI = "https://doi.org/10.1109/CDC.2009.5400036",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5400036",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695",
}
@InProceedings{Nazin:2009:RAFa,
author = "A. Nazin and B. Polyak",
booktitle = "{ISIC 2009: IEEE Control Applications, (CCA) \&
Intelligent Control}",
title = "A randomized algorithm for finding eigenvector of
stochastic matrix with application to {PageRank}
problem",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "412--416",
year = "2009",
DOI = "https://doi.org/10.1109/CCA.2009.5280707",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5280707",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5268173",
}
@Article{Nazin:2009:RAFb,
author = "A. V. Nazin and B. T. Polyak",
title = "A randomized algorithm for finding an eigenvector of a
stochastic matrix with application to {PageRank}",
journal = j-DOKL-AKAD-NAUK,
volume = "426",
number = "6",
pages = "734--737",
year = "2009",
CODEN = "DANKAS",
ISSN = "0869-5652",
MRclass = "62L20 (15A18 15B51)",
MRnumber = "MR2573029",
bibdate = "Wed May 5 19:28:06 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "English translation in Dokl. Math. 79(3) 424--427
(2009).",
acknowledgement = ack-nhfb,
fjournal = "Rossi\u\i skaya Akademiya Nauk. Doklady Akademii
Nauk",
}
@InProceedings{Phuoc:2009:PVK,
author = "Nguyen Quang Phuoc and Sung-Ryul Kim and Han-Ku Lee
and Hyung Seok Kim",
booktitle = "{ICCIT '09: Fourth International Conference on
Computer Sciences and Convergence Information
Technology (2009)}",
title = "{PageRank} vs. {Katz Status Index}, a Theoretical
Approach",
crossref = "Sohn:2009:FIC",
pages = "1276--1279",
year = "2009",
DOI = "https://doi.org/10.1109/ICCIT.2009.272",
ISBN = "0-7695-3896-7",
ISBN-13 = "978-0-7695-3896-9",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5368419",
abstract = "In World Wide Web search engines, it is important to
have a good ranking system. One of the most famous
ranking components is the PageRank system by Google.
However, PageRank is protected by patents and it is
impossible for other companies to use it in their
search engines. There is an old model, called Katz
status index, that is reported to work very similar to
PageRank. If the quality of Katz status index turns out
to be similar to or better than that of PageRank, it
could become a patent-free alternative to PageRank. We
consider the problem of comparing Katz status index to
PageRank in this paper with some preliminary results on
the theoretical comparison and give a proposal for
practical comparison of the two models.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5367867",
keywords = "Katz status index; RageRank; search engines; World
Wide Web",
}
@InCollection{Rousseau:2009:GAP,
author = "Christiane Rousseau and Yvan Saint-Aubin",
title = "{Google} et l'algorithme {PageRank}",
crossref = "Rousseau:2009:MT",
pages = "273--297",
year = "2009",
DOI = "https://doi.org/10.1007/978-0-387-69213-5_9",
bibdate = "Tue Jul 20 16:43:36 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
language = "French",
}
@InProceedings{Su:2009:PHI,
author = "Cheng Su and YunTao Pan and JunPeng Yuan and Hong Guo
and ZhengLu Yu and ZhiYu Hu",
booktitle = "{2009 WRI World Congress on Computer Science and
Information Engineering}",
title = "{PageRank}, {HITS} and Impact Factor for Journal
Ranking",
crossref = "IEEE:2009:PWW",
volume = "6",
pages = "285--290",
year = "2009",
DOI = "https://doi.org/10.1109/CSIE.2009.351",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5170706",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5170260",
}
@Article{Vigna:2009:SR,
author = "Sebastiano Vigna",
title = "Spectral Ranking",
journal = "arxiv.org",
volume = "arXiv:0912.0238 [cs.IR]",
pages = "1--13",
day = "1",
month = dec,
year = "2009",
bibdate = "Tue Aug 11 17:40:40 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://arxiv.org/abs/0912.0238",
abstract = "This note tries to attempt a sketch of the history of
spectral ranking, a general umbrella name for
techniques that apply the theory of linear maps (in
particular, eigenvalues and eigenvectors) to matrices
that do not represent geometric transformations, but
rather some kind of relationship between entities.
Albeit recently made famous by the ample press coverage
of Google's PageRank algorithm, spectral ranking was
devised more than sixty years ago, almost exactly in
the same terms, and has been studied in psychology and
social sciences. I will try to describe it in precise
and modern mathematical terms, highlighting along the
way the contributions given by previous scholars.",
acknowledgement = ack-nhfb,
}
@InProceedings{Wan:2009:IPA,
author = "Jing Wan and Si-Xue Bai",
booktitle = "{GRC '09: IEEE International Conference on Granular
Computing (2009)}",
title = "An improvement of {PageRank} algorithm based on the
time-activity-curve",
crossref = "Zhang:2006:IIC",
pages = "549--552",
year = "2009",
DOI = "https://doi.org/10.1109/GRC.2009.5255060",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5255060",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5234367",
}
@Article{Wills:2009:ORG,
author = "Rebecca S. Wills and Ilse C. F. Ipsen",
title = "Ordinal Ranking for {Google}'s {PageRank}",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "30",
number = "4",
pages = "1677--1696",
month = "????",
year = "2009",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/070698129",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
MRclass = "62F07 (65F15 68P20)",
MRnumber = "2486859 (2010d:62041)",
MRreviewer = "Truc Nguyen",
bibdate = "Tue May 18 22:32:31 MDT 2010",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toclist/SIMAX/;
https://www.math.utah.edu/pub/bibnet/authors/i/ipsen-ilse-c-f.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
}
@Article{Xiong:2009:ESR,
author = "Zhiping Xiong and Bing Zheng",
title = "On the eigenvalues of a specially rank-$r$ updated
complex matrix",
journal = j-COMPUT-MATH-APPL,
volume = "57",
number = "10",
pages = "1645--1650",
month = may,
year = "2009",
CODEN = "CMAPDK",
DOI = "https://doi.org/10.1016/j.camwa.2009.02.027",
ISSN = "0898-1221 (print), 1873-7668 (electronic)",
ISSN-L = "0898-1221",
bibdate = "Thu Dec 29 08:16:04 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0898122109002247",
acknowledgement = ack-nhfb,
fjournal = "Computers and Mathematics with Applications",
keywords = "PageRank",
}
@InProceedings{Yen:2009:API,
author = "Chia-Chen Yen and Jih-Shih Hsu",
editor = "{IEEE}",
booktitle = "{VECIMS '09: IEEE International Conference on Virtual
Environments, Human-Computer Interfaces and
Measurements Systems (2009), May 11--13, 2009, Hong
Kong, China}",
title = "Associated {PageRank}: Improved {PageRank} measured by
frequent term sets",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "282--286",
year = "2009",
DOI = "https://doi.org/10.1109/VECIMS.2009.5068909",
ISBN = "1-4244-3808-X",
ISBN-13 = "978-1-4244-3808-2",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5068909",
abstract = "Web search engines encounter many new challenges while
the amount of information on the web increases rapidly.
Web documents have been a main resource for various
purposes, and people rely on search engines to retrieve
the desired documents. This paper proposes an
associated pagerank algorithm for search engines to
feedback quality results by scoring the relevance of
web documents. The modified Pagerank algorithm
increases the degree of relevance than the original
one, and decreases the query time efforts of
topic-sensitive pagerank.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5038837",
keywords = "document relevance; information retrieval; pagerank;
topic-sensitive; web search",
}
@InProceedings{Yen:2009:PAI,
author = "Chia-Chen Yen and Jih-Shih Hsu",
booktitle = "{FUZZ-IEEE 2009: IEEE International Conference on
Fuzzy Systems}",
title = "{PageRank} algorithm improvement by page relevance
measurement",
crossref = "IEEE:2009:IIC",
pages = "502--506",
year = "2009",
DOI = "https://doi.org/10.1109/FUZZY.2009.5277414",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5277414",
abstract = "Pagerank algorithm evaluates the importance of web
pages by the link analysis, and there are many
techniques to improve the traditional pagerank
algorithm to prevent from the biases of link spamming
in recent years. The modified algorithms should concern
not only the correctness, but also the efficiency
should be considered. This paper proposes an associated
pagerank algorithm for search engines to feedback
quality results by scoring the relevance between web
documents. The modified Pagerank algorithm increases
the degree of relevance than the original one, and
decreases the query time efforts of topic-sensitive
pagerank.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5247842",
keywords = "document relevance; information retrieval; pagerank;
topic-sensitive; Web search",
}
@InProceedings{Zhang:2009:IPW,
author = "Ling Zhang and Zheng Qin",
booktitle = "{2009 1st International Conference on Information
Science and Engineering (ICISE)}",
title = "The Improved {Pagerank} in {Web} Crawler",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "1889--1892",
year = "2009",
DOI = "https://doi.org/10.1109/ICISE.2009.1220",
ISBN = "1-4244-4909-X",
ISBN-13 = "978-1-4244-4909-5",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5455065",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5454173",
}
@InProceedings{Zheng:2009:LSP,
author = "Ling Zheng and Ning Zhang and Yang Bo",
editor = "{IEEE}",
booktitle = "{ICISE '09: Proceedings of the 2009 First IEEE
International Conference on Information Science and
Engineering}",
title = "Link-Sensitive {PageRank}: An Improved Ranking
Algorithm for Vertical Search Engines",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "887--890",
year = "2009",
DOI = "https://doi.org/10.1109/ICISE.2009.715",
ISBN = "0-7695-3887-8",
ISBN-13 = "978-0-7695-3887-7",
LCCN = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5455348",
abstract = "The PageRank algorithm is an important link-based
ranking strategy of vertical search engines, but it has
a drawback of topic drift. To tackle this problem and
yield more accurate search results, we present an
improved algorithm to distribute the PageRank value in
light of the link sensitive level of the web pages
based on keywords set, which we called 'Link-Sensitive
PageRank'. According to the keywords of user's
searching, this algorithm, which takes into account the
link sensitive level of the web pages' hyperlink to
give different importance to different hyperlinks.
Experiment results show that the improved PageRank
algorithm performs better than the standard PageRank.
Furthermore, it can effectively improve the 'topic
drift' and enhance the accuracy of information
collection. The proposed PageRank algorithm can have a
good application in the vertical search engines.",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5454173",
}
@Article{Altman:2010:AAP,
author = "Alon Altman and Moshe Tennenholtz",
title = "An axiomatic approach to personalized ranking
systems",
journal = j-J-ACM,
volume = "57",
number = "4",
pages = "26:1--26:35",
month = apr,
year = "2010",
CODEN = "JACOAH",
DOI = "https://doi.org/10.1145/1734213.1734220",
ISSN = "0004-5411 (print), 1557-735X (electronic)",
ISSN-L = "0004-5411",
bibdate = "Thu Apr 29 13:26:36 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Personalized ranking systems and trust systems are an
essential tool for collaboration in a multi-agent
environment. In these systems, trust relations between
many agents are aggregated to produce a personalized
trust rating of the agents. In this article, we
introduce the first extensive axiomatic study of this
setting, and explore a wide array of well-known and new
personalized ranking systems. We adapt several axioms
(basic criteria) from the literature on global ranking
systems to the context of personalized ranking systems,
and fully classify the set of systems that satisfy all
of these axioms. We further show that all these axioms
are necessary for this result.",
acknowledgement = ack-nhfb,
articleno = "26",
fjournal = "Journal of the ACM",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J401",
keywords = "Advogato; Axiomatic approach; e-Bay reputation system;
epinions.com; manipulation; MoleTrust; OpenPGP;
PageRank; ranking systems; social networks",
}
@Article{Bahmani:2010:FIP,
author = "Bahman Bahmani and Abdur Chowdhury and Ashish Goel",
title = "Fast incremental and personalized {PageRank}",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "4",
number = "3",
pages = "173--184",
month = dec,
year = "2010",
CODEN = "????",
ISSN = "2150-8097",
bibdate = "Fri May 13 14:55:16 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In this paper, we analyze the efficiency of Monte
Carlo methods for incremental computation of PageRank,
personalized PageRank, and similar random walk based
methods (with focus on SALSA), on large-scale
dynamically evolving social networks. We assume that
the graph of friendships is stored in distributed
shared memory, as is the case for large social networks
such as Twitter.\par
For global PageRank, we assume that the social network
has $n$ nodes, and $m$ adversarially chosen edges
arrive in a random order. We show that with a reset
probability of $ \epsilon $, the expected total work
needed to maintain an accurate estimate (using the
Monte Carlo method) of the PageRank of every node at
all times is $ O(n \ln m / \epsilon^2)$. This is
significantly better than all known bounds for
incremental PageRank. For instance, if we naively
recompute the PageRanks as each edge arrives, the
simple power iteration method needs $ \Omega (m^2 / \ln
(1 / (1 - \epsilon)))$ total time and the Monte Carlo
method needs $ O(m n / \epsilon)$ total time; both are
prohibitively expensive. We also show that we can
handle deletions equally efficiently.\par
We then study the computation of the top $k$
personalized PageRanks starting from a seed node,
assuming that personalized PageRanks follow a power-law
with exponent $ < 1$. We show that if we store $ R > q
\ln n$ random walks starting from every node for large
enough constant q (using the approach outlined for
global PageRank), then the expected number of calls
made to the distributed social network database is $
O(k / (R^{(1 - \alpha) / \alpha }))$. We also present
experimental results from the social networking site,
Twitter, verifying our assumptions and analyses. The
overall result is that this algorithm is fast enough
for real-time queries over a dynamic social network.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
}
@Article{Bini:2010:CAE,
author = "Dario A. Bini and Gianna M. {Del Corso} and F.
Romani",
title = "A combined approach for evaluating papers, authors and
scientific journals",
journal = j-J-COMPUT-APPL-MATH,
volume = "234",
number = "11",
pages = "3104--3121",
day = "1",
month = oct,
year = "2010",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2010.02.003",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Wed Aug 12 08:08:51 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042710000749",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
keywords = "PageRank",
}
@InProceedings{Chen:2010:PSC,
author = "Yao Chen and Wenjun Xiong and Jinhu Lu and D. W. C.
Ho",
booktitle = "{2010 International Conference on Intelligent
Computing and Integrated Systems (ICISS)}",
title = "Pinning scheme for complex networks based on
{PageRank} Algorithm",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "709--712",
year = "2010",
DOI = "https://doi.org/10.1109/ICISS.2010.5657148",
ISBN = "",
ISBN-13 = "",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5657148",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5643978",
}
@Article{Cicone:2010:GPP,
author = "Antonio Cicone and Stefano Serra-Capizzano",
title = "{Google} {PageRanking} problem: the model and the
analysis",
journal = j-J-COMPUT-APPL-MATH,
volume = "234",
number = "11",
pages = "3140--3169",
day = "1",
month = oct,
year = "2010",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:24:23 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042710000762",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Book{Clifton:2010:AWM,
author = "Brian Clifton",
title = "Advanced {Web} metrics with {Google Analytics}",
publisher = pub-WILEY,
address = pub-WILEY:adr,
edition = "Second",
pages = "xxv + 501",
year = "2010",
ISBN = "0-470-56231-5",
ISBN-13 = "978-0-470-56231-4",
LCCN = "TK5105.885.G66 C55 2010eb",
bibdate = "Fri Jun 3 09:52:48 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
acknowledgement = ack-nhfb,
subject = "Google Analytics; Web usage mining; Internet users;
Statistics; Data processing",
}
@Article{Constantine:2010:RAP,
author = "P. G. Constantine and D. F. Gleich",
title = "Random alpha {PageRank}",
journal = j-INTERNET-MATH,
volume = "6",
number = "2",
pages = "189--236",
month = "????",
year = "2010",
CODEN = "????",
ISSN = "1542-7951 (print), 1944-9488 (electronic)",
ISSN-L = "1542-7951",
bibdate = "Tue Aug 11 16:34:18 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://projecteuclid.org/euclid.im/1285339073",
acknowledgement = ack-nhfb,
fjournal = "Internet Mathematics",
journal-URL = "http://projecteuclid.org/info/euclid.im",
}
@Book{Croft:2010:SEI,
author = "W. Bruce Croft and Donald Metzler and Trevor
Strohman",
title = "Search engines: information retrieval in practice",
publisher = "Pearson Education",
address = "Boston, MA, USA",
pages = "xxv + 524",
year = "2010",
ISBN = "0-13-136489-8 (paperback)",
ISBN-13 = "978-0-13-136489-9 (paperback)",
LCCN = "TK5105.884 CRO 2010",
bibdate = "Thu May 5 19:23:28 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
library.ox.ac.uk:210/ADVANCE",
acknowledgement = ack-nhfb,
subject = "Search engines; Information storage and retrieval
systems; Information retrieval",
}
@TechReport{Franceschet:2010:PSS,
author = "Massimo Franceschet",
title = "{PageRank}: Stand on the shoulders of giants",
type = "Report",
institution = "Department of Mathematics and Computer Science,
University of Udine",
address = "Via delle Scienze 206, 33100 Udine, Italy",
pages = "21",
year = "2010",
bibdate = "Fri Feb 19 15:07:14 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://arxiv.org/pdf/1002.2858",
abstract = "PageRank is a Web page ranking technique that
radically changed the concepts of quality and truth of
information found on the web. The method was devel-
oped by Sergey Brin and Larry Page while studying at
Stanford University and is currently an important
ingredient of Google search engine. The main idea
behind PageRank is to determine the importance of a Web
page in terms of the very same notion of importance
assigned to the pages hyperlinking to it. In fact, this
thesis in not new, and has been previously successfully
exploited in different contexts. In this work, we
review the PageRank method and link it to some renowned
predecessors we have found in the fields of Web
information retrieval, bibliometrics, sociology, and
economics.",
acknowledgement = ack-nhfb,
keywords = "bibliometrics; commodity pricing; PageRank; social
network analysis; Web information retrieval",
}
@Article{Gleich:2010:IOI,
author = "David F. Gleich and Andrew P. Gray and Chen Greif and
Tracy Lau",
title = "An Inner-Outer Iteration for Computing {PageRank}",
journal = j-SIAM-J-SCI-COMP,
volume = "32",
number = "1",
pages = "349--371",
month = "????",
year = "2010",
CODEN = "SJOCE3",
DOI = "https://doi.org/10.1137/080727397",
ISSN = "1064-8275 (print), 1095-7197 (electronic)",
ISSN-L = "1064-8275",
bibdate = "Wed May 19 10:44:24 MDT 2010",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SISC/32/1;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "We present a new iterative scheme for PageRank
computation. The algorithm is applied to the linear
system formulation of the problem, using inner-outer
stationary iterations. It is simple, can be easily
implemented and parallelized, and requires minimal
storage overhead. Our convergence analysis shows that
the algorithm is effective for a crude inner tolerance
and is not sensitive to the choice of the parameters
involved. The same idea can be used as a
preconditioning technique for nonstationary schemes.
Numerical examples featuring matrices of dimensions
exceeding 100,000,000 in sequential and parallel
environments demonstrate the merits of our technique.
Our code is available online for viewing and testing,
along with several large scale examples.",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Scientific Computing",
journal-URL = "http://epubs.siam.org/sisc",
}
@InProceedings{Gleich:2010:TRS,
author = "David F. Gleich and Paul G. Constantine and Abraham
Flaxman and Asela Gunawardana",
editor = "Michael Rappa and Paul Jones",
booktitle = "{Proceedings of the 19th International Conference on
World Wide Web: Raleigh, North Carolina, USA, April
26--30, 2010}",
title = "Tracking the random surfer: Empirically measured
teleportation parameters in {PageRank}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "381--390",
year = "2010",
DOI = "https://doi.org/10.1145/1772690.1772730",
ISBN = "1-60558-799-0",
ISBN-13 = "978-1-60558-799-8",
LCCN = "TK5105.888 .I573 2010eb",
bibdate = "Tue Aug 11 16:45:55 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1145/1772690",
bookpages = "41 + 1386",
}
@InProceedings{He:2010:WBL,
author = "Xiaojun He and Yibing Li and Chunxiao Fan",
booktitle = "{2010 International Conference on E-Business and
E-Government (ICEE)}",
title = "{Web}-Based Links and Authoritative Content {Pagerank}
Improvement",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "5016--5019",
year = "2010",
DOI = "https://doi.org/10.1109/ICEE.2010.1259",
ISBN = "0-7695-3997-1",
ISBN-13 = "978-0-7695-3997-3",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5592871",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5589107",
}
@Article{Ishii:2010:DRA,
author = "H. Ishii and R. Tempo",
title = "Distributed Randomized Algorithms for the {PageRank}
Computation",
journal = j-IEEE-TRANS-AUTOMAT-CONTR,
volume = "55",
number = "9",
pages = "1987--2002",
month = "????",
year = "2010",
CODEN = "IETAA9",
DOI = "https://doi.org/10.1109/TAC.2010.2042984",
ISSN = "0018-9286",
ISSN-L = "0018-9286",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5411738",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9",
fjournal = "IEEE Transactions on Automatic Control",
}
@InProceedings{Ishii:2010:DRP,
author = "H. Ishii and R. Tempo and E. Bai",
booktitle = "{2010 49th IEEE Conference on Decision and Control
(CDC)}",
title = "Distributed randomized pagerank algorithms based on
web aggregation over unreliable channels",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "6602--6607",
year = "2010",
DOI = "https://doi.org/10.1109/CDC.2010.5718041",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5718041",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5707200",
}
@InProceedings{Jain:2010:FRW,
author = "Alpa Jain and Patrick Pantel",
editor = "Chu-Ren Huang and Dan Jurafsky",
booktitle = "{COLING'10: 23rd International Conference on
Computational Linguistics, Proceedings, 23--27 August
2010, Beijing International Convention Center, Beijing,
China}",
title = "{FactRank}: Random walks on a web of facts",
publisher = "Tsinghua University Press",
address = "Block A, Xue Yan Building, Tsinghua University,
Beijing, 100084, China",
pages = "501--509",
month = aug,
year = "2010",
ISBN = "????",
ISBN-13 = "????",
LCCN = "????",
bibdate = "Tue Aug 11 17:06:19 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://delivery.acm.org/10.1145/1880000/1873838/p501-jain.pdf",
acknowledgement = ack-nhfb,
xxaddress = "Stroudsburg, PA, USA",
xxpublisher = "Association for Computational Linguistics",
}
@Article{Jiang:2010:TRB,
author = "Wei Jiang and Gang Wu",
title = "A thick-restarted block {Arnoldi} algorithm with
modified {Ritz} vectors for large eigenproblems",
journal = j-COMPUT-MATH-APPL,
volume = "60",
number = "3",
pages = "873--889",
month = aug,
year = "2010",
CODEN = "CMAPDK",
DOI = "https://doi.org/10.1016/j.camwa.2010.05.034",
ISSN = "0898-1221 (print), 1873-7668 (electronic)",
ISSN-L = "0898-1221",
bibdate = "Thu Dec 29 08:18:39 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0898122110003913",
acknowledgement = ack-nhfb,
fjournal = "Computers and Mathematics with Applications",
}
@Book{Kamvar:2010:NAP,
author = "Sep Kamvar",
title = "Numerical algorithms for personalized search in
self-organizing information networks",
publisher = pub-PRINCETON,
address = pub-PRINCETON:adr,
pages = "xiv + 139",
year = "2010",
ISBN = "0-691-14503-2 (hardcover)",
ISBN-13 = "978-0-691-14503-7 (hardcover)",
LCCN = "ZA4460 .K36 2010",
bibdate = "Mon Jun 13 18:50:45 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
abstract = "This book lays out the theoretical groundwork for
personalized search and reputation management, both on
the Web and in peer-to-peer and social networks.'' The
book develops scalable algorithms that exploit the
graphlike properties underlying personalized search and
reputation management, and delves into realistic
scenarios regarding web-scale data",
acknowledgement = ack-nhfb,
subject = "Database searching; Mathematics; Information networks;
Content analysis (Communication); Self-organizing
systems; Data processing; Algorithms; Internet
searching",
tableofcontents = "World Wide Web \\
PageRank \\
The second eigenvalue of the Google Matrix \\
The condition number of the pagerank problem \\
Extrapolation algorithms \\
Adaptive pagerank \\
BlockRank \\
P2P networks. Query-cycle simulator \\
EigenTrust \\
Adaptive P2P topologies",
}
@Article{Kurland:2010:PHS,
author = "Oren Kurland and Lillian Lee",
title = "{PageRank} without hyperlinks: {Structural} reranking
using links induced by language models",
journal = j-TOIS,
volume = "28",
number = "4",
pages = "18:1--18:??",
month = nov,
year = "2010",
CODEN = "ATISET",
DOI = "https://doi.org/10.1145/1852102.1852104",
ISSN = "1046-8188",
ISSN-L = "0734-2047",
bibdate = "Tue Nov 23 10:24:49 MST 2010",
bibsource = "http://www.acm.org/pubs/contents/journals/tois/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
articleno = "18",
fjournal = "ACM Transactions on Information Systems (TOIS)",
}
@Book{Ledford:2010:GA,
author = "Jerri L. Ledford and Joe Teixeira and Mary E. Tyler",
title = "{Google Analytics}",
publisher = pub-WILEY,
address = pub-WILEY:adr,
edition = "Third",
pages = "xxvii + 404",
year = "2010",
ISBN = "0-470-53128-2, 0-470-87400-7",
ISBN-13 = "978-0-470-53128-0, 978-0-470-87400-4",
LCCN = "TK5105.885.G66 L43 2010eb",
bibdate = "Fri Jun 3 09:52:48 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
acknowledgement = ack-nhfb,
subject = "Google Analytics; Internet searching; Statistical
services; Web usage mining; Computer programs; Internet
users; Statistics; Data processing",
tableofcontents = ". Part 1. Getting started with Google Analytics \\
Part 2. Analytics and site statistics: concepts and
methods \\
Part 3. Advanced implementation \\
Part 4. The reports",
}
@Article{Levy:2010:HGA,
author = "Steven Levy",
title = "How {Google}'s algorithm rules the {Web}",
journal = j-WIRED,
volume = "17",
pages = "??--??",
day = "2",
month = feb,
year = "2010",
CODEN = "WREDEM",
ISSN = "1059-1028 (print), 1078-3148 (electronic)",
ISSN-L = "1059-1028",
bibdate = "Tue Aug 11 17:21:08 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.wired.com/2010/02/ff_google_algorithm/",
acknowledgement = ack-nhfb,
fjournal = "Wired",
journal-URL = "http://www.wired.com",
}
@InProceedings{Liu:2010:KEU,
author = "Zhengyang Liu and Jianyi Liu and Wenbin Yao and Cong
Wang",
booktitle = "{2010 International Conference on E-Product E-Service
and E-Entertainment (ICEEE)}",
title = "Keyword Extraction Using {PageRank} on Synonym
Networks",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "1--4",
year = "2010",
DOI = "https://doi.org/10.1109/ICEEE.2010.5660630",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5660630",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5660084",
}
@InProceedings{Liu:2010:OMP,
author = "Dongfei Liu and Yong Gong",
booktitle = "{2010 2nd International Conference on Computer
Engineering and Technology (ICCET)}",
title = "Optimal methods of {PageRank} algorithm on the
bilingual web page",
volume = "1",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "V1--689--V1--691",
year = "2010",
DOI = "https://doi.org/10.1109/ICCET.2010.5485388",
ISBN = "1-4244-6347-5",
ISBN-13 = "978-1-4244-6347-3",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5485388",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5473895",
}
@InProceedings{Ma:2010:RPA,
author = "Haibo Ma and Shiyong Chen and Deguang Wang",
booktitle = "{2010 International Conference on Web Information
Systems and Mining (WISM)}",
title = "Research of {PageRank} Algorithm Based on Transition
Probability",
volume = "1",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "153--155",
year = "2010",
DOI = "https://doi.org/10.1109/WISM.2010.63",
ISBN = "1-4244-8438-3",
ISBN-13 = "978-1-4244-8438-6",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5662302",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5661667",
}
@InProceedings{McGettrick:2010:HCP,
author = "S. McGettrick and D. Geraghty",
booktitle = "{2010 International Conference on Reconfigurable
Computing and FPGAs (ReConFig)}",
title = "Hardware Computation of the {PageRank} Eigenvector",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "256--261",
year = "2010",
DOI = "https://doi.org/10.1109/ReConFig.2010.83",
ISBN = "1-4244-9523-7",
ISBN-13 = "978-1-4244-9523-8",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5695315",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5692850",
}
@InProceedings{Nazin:2010:EPE,
author = "A. Nazin",
booktitle = "{2010 49th IEEE Conference on Decision and Control
(CDC)}",
title = "Estimating the principal eigenvector of a stochastic
matrix: Mirror Descent Algorithms via game approach
with application to {PageRank} problem",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "792--797",
year = "2010",
DOI = "https://doi.org/10.1109/CDC.2010.5717923",
ISBN = "????",
ISBN-13 = "????",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5717923",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5707200",
}
@InProceedings{Pu:2010:IPA,
author = "Bing-Yuan Pu and Ting-Zhu Huang and Chun Wen",
booktitle = "{2010 4th International Conference on Network and
System Security (NSS)}",
title = "An Improved {PageRank} Algorithm: Immune to Spam",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "425--429",
year = "2010",
DOI = "https://doi.org/10.1109/NSS.2010.12",
ISBN = "1-4244-8484-7",
ISBN-13 = "978-1-4244-8484-3",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5635820",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5634608",
}
@InProceedings{Qin:2010:BRA,
author = "Yongbin Qin and Daoyun Xu",
booktitle = "{2010 2nd International Workshop on Intelligent
Systems and Applications (ISA)}",
title = "A Balanced Rank Algorithm Based on {PageRank} and Page
Belief Recommendation",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "1--4",
year = "2010",
DOI = "https://doi.org/10.1109/IWISA.2010.5473657",
ISBN = "1-4244-5872-2",
ISBN-13 = "978-1-4244-5872-1",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5473657",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5472913",
}
@Book{Rhodes:2010:CLB,
editor = "Brett D. Rhodes",
title = "Copyright law and a brief look at the {Google Library
Project}",
publisher = "Nova Science Publishers",
address = "New York, NY, USA",
pages = "xi + 166",
year = "2010",
ISBN = "1-60741-871-1 (hardcover)",
ISBN-13 = "978-1-60741-871-9 (hardcover)",
LCCN = "KF2994 .C62 2010",
bibdate = "Fri Jun 3 09:47:20 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
series = "Laws and legislation",
acknowledgement = ack-nhfb,
subject = "Copyright; United States; Fair use (Copyright)",
tableofcontents = "Copyright law: second edition / Robert A. Gorman,
Kenneth W. Gemmill \\
The Google Library Project: is digitization for
purposes of online indexing fair use under copyright
law? / Kate M. Manuel \\
Internet search engines: copyright's ``fair use'' in
reproduction and public display rights / Robin Jeweler,
Brian T. Yeh",
}
@Article{Shepelyansky:2010:GMD,
author = "D. L. Shepelyansky and O. V. Zhirov",
title = "{Google} matrix, dynamical attractors, and {Ulam}
networks",
journal = j-PHYS-REV-E,
volume = "81",
number = "3",
pages = "036213:1--036213:9",
month = mar,
year = "2010",
CODEN = "PLEEE8",
DOI = "https://doi.org/10.1103/PhysRevE.81.036213",
ISSN = "1539-3755 (print), 1550-2376 (electronic)",
ISSN-L = "1539-3755",
bibdate = "Tue Aug 11 17:34:23 2015",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/u/ulam-stanislaw-m.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://journals.aps.org/pre/abstract/10.1103/PhysRevE.81.036213;
http://link.aps.org/doi/10.1103/PhysRevE.81.036213",
acknowledgement = ack-nhfb,
fjournal = "Physical Review E (Statistical physics, plasmas,
fluids, and related interdisciplinary topics)",
journal-URL = "http://pre.aps.org/browse",
}
@InProceedings{Wang:2010:APA,
author = "Deguang Wang and Zhigang Zhou and Haibo Ma",
booktitle = "{2010 Second International Conference on Information
Technology and Computer Science (ITCS)}",
title = "Application of {PageRank} Algorithm in Computer
Forensics",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "250--253",
year = "2010",
DOI = "https://doi.org/10.1109/ITCS.2010.68",
ISBN = "1-4244-7293-8",
ISBN-13 = "978-1-4244-7293-2",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5557139",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5556872",
}
@InProceedings{Weng:2010:TFT,
author = "Jianshu Weng and Ee-Peng Lim and Jing Jiang and Qi
He",
editor = "Brian D. Davison and Torsten Suel",
booktitle = "{WSDM: proceedings of the third ACM International
Conference on Web Search and Data Mining: February
3--6, 2010, New York City, NY, USA}",
title = "{TwitterRank}: Finding topic-sensitive influential
twitterers",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "261--270",
year = "2010",
DOI = "https://doi.org/10.1145/1718487.1718520",
ISBN = "1-60558-889-X",
ISBN-13 = "978-1-60558-889-6",
LCCN = "QA76.9.D343 I5838 2010",
bibdate = "Tue Aug 11 17:45:37 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1145/1718487",
book-URL = "http://portal.acm.org/toc.cfm?id=1718487",
bookpages = "xii + 450",
}
@Article{Wu:2010:AEA,
author = "Gang Wu and Yimin Wei",
title = "An {Arnoldi}-extrapolation algorithm for computing
{PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "234",
number = "11",
pages = "3196--3212",
day = "1",
month = oct,
year = "2010",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:24:23 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042710000804",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Wu:2010:AVG,
author = "Gang Wu and Yimin Wei",
title = "{Arnoldi} versus {GMRES} for computing {PageRank}: a
theoretical contribution to {Google}'s {PageRank}
problem",
journal = j-TOIS,
volume = "28",
number = "3",
pages = "11:1--11:28",
month = jun,
year = "2010",
CODEN = "ATISET",
DOI = "https://doi.org/10.1145/1777432.1777434",
ISSN = "1046-8188",
ISSN-L = "0734-2047",
bibdate = "Tue Jul 6 15:53:00 MDT 2010",
bibsource = "http://www.acm.org/pubs/contents/journals/tois/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "PageRank is one of the most important ranking
techniques used in today's search engines. A recent
very interesting research track focuses on exploiting
efficient numerical methods to speed up the computation
of PageRank, among which the Arnoldi-type algorithm and
the GMRES algorithm are competitive candidates. In
essence, the former deals with the PageRank problem
from an eigenproblem, while the latter from a linear
system, point of view. However, there is little known
about the relations between the two approaches for
PageRank. In this article, we focus on a theoretical
and numerical comparison of the two approaches.
Numerical experiments illustrate the effectiveness of
our theoretical results.",
acknowledgement = ack-nhfb,
articleno = "11",
fjournal = "ACM Transactions on Information Systems",
keywords = "Arnoldi; GMRES; Google; Krylov subspace; PageRank; Web
ranking",
}
@InProceedings{Wu:2010:EPS,
author = "Tianji Wu and Bo Wang and Yi Shan and Feng Yan and Yu
Wang and Ningyi Xu",
booktitle = "{2010 39th International Conference on Parallel
Processing (ICPP)}",
title = "Efficient {PageRank} and {SpMV} Computation on {AMD}
{GPUs}",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "81--89",
year = "2010",
DOI = "https://doi.org/10.1109/ICPP.2010.17",
ISBN = "1-4244-7913-4",
ISBN-13 = "978-1-4244-7913-9",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5599152",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5598250",
}
@Article{Wu:2010:KSA,
author = "Gang Wu and Ying Zhang and Yimin Wei",
title = "{Krylov} Subspace Algorithms for Computing {GeneRank}
for the Analysis of Microarray Data Mining",
journal = j-J-COMPUT-BIOL,
volume = "17",
number = "4",
pages = "631--646",
month = apr,
year = "2010",
CODEN = "JCOBEM",
DOI = "https://doi.org/10.1089/cmb.2009.0004",
ISSN = "1066-5277 (print), 1557-8666 (electronic)",
ISSN-L = "1066-5277",
bibdate = "Sat Jun 1 09:49:51 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputbiol.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.liebertpub.com/doi/abs/10.1089/cmb.2009.0004;
https://www.liebertpub.com/doi/pdf/10.1089/cmb.2009.0004",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational Biology",
journal-URL = "https://www.liebertpub.com/loi/cmb/",
onlinedate = "28 April 2010",
}
@InProceedings{Zhang:2010:MSF,
author = "Yi Zhang and Kaihua Xu and Yuhua Liu and Zhenrong
Luo",
editor = "{IEEE}",
booktitle = "{Proceedings of the 2010 2nd International Conference
on Future Computer and Communication: ICFCC 2010, 21-24
May 2010, Wuhan, China}",
title = "Modeling of scale-free network based on pagerank
algorithm",
volume = "3",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "V3--783--V3--786",
year = "2010",
DOI = "https://doi.org/10.1109/ICFCC.2010.5497402",
ISBN = "1-4244-5822-6, 1-4244-5821-8",
ISBN-13 = "978-1-4244-5822-6, 978-1-4244-5821-9",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "IEEE catalog number CFP1037G-PRT.",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5497402",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5487607",
}
@InProceedings{Zhang:2010:WRM,
author = "Ji-Lin Zhang and Yong-jian Ren and Wei Zhang and
Xiang-Hua Xu and Jian Wan and Yu Weng",
booktitle = "{2010 2nd International Conference on Information
Science and Engineering (ICISE)}",
title = "Webs ranking model based on pagerank algorithm",
publisher = "pub-IEEE",
address = "pub-IEEE:adr",
pages = "4811--4814",
year = "2010",
DOI = "https://doi.org/10.1109/ICISE.2010.5691573",
ISBN = "1-4244-7616-X",
ISBN-13 = "978-1-4244-7616-9",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5691573",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5680733",
}
@Misc{Anonymous:2011:EOR,
author = "Anonymous",
title = "{{\tt eigenfactor.org}}: Ranking and mapping science",
howpublished = "Web site.",
year = "2011",
bibdate = "Thu Jun 02 08:43:09 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "Journal impact ranking.",
URL = "http://www.eigenfactor.org/",
acknowledgement = ack-nhfb,
}
@Book{Bailyn:2011:OG,
author = "Evan Bailyn and Brad Bailyn",
title = "Outsmarting {Google}",
publisher = pub-QUE,
address = pub-QUE:adr,
pages = "xi + 226",
year = "2011",
ISBN = "0-7897-4103-2",
ISBN-13 = "978-0-7897-4103-5",
LCCN = "HD9696.8.U64 G6627 2011",
bibdate = "Fri Jun 3 09:52:48 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
acknowledgement = ack-nhfb,
subject = "Electronic commerce; Internet searching; Web search
engines; Success in business",
tableofcontents = "Trust: the currency of Google \\
The five ingredients of Google optimization \\
How to reel in links \\
Using time to gain trust \\
The nuclear football \\
Google AdWords as a complement to SEO \\
Tracking your progress with search operators \\
Google optimization myths \\
White hat versus black hat SEO \\
Optimizing for Yahoo! and Bing \\
Converting your SEO results into paying customers \\
The intersection of social media and SEO \\
The future of SEO.",
}
@InProceedings{Cailan:2011:IPA,
author = "Zhou Cailan and Chen Kai and Li Shasha",
booktitle = "{2011 International Conference on Computer Science and
Service System (CSSS)}",
title = "Improved {PageRank} algorithm based on feedback of
user clicks",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "3949--3952",
year = "2011",
DOI = "https://doi.org/10.1109/CSSS.2011.5974627",
bibdate = "Mon Sep 12 21:28:08 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5974627",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5959270",
}
@Article{Cevahir:2011:SBP,
author = "Ali Cevahir and Cevdet Aykanat and Ata Turk and B.
Barla Cambazo{\u{g}}glu",
title = "Site-Based Partitioning and Repartitioning Techniques
for Parallel {PageRank} Computation",
journal = j-IEEE-TRANS-PAR-DIST-SYS,
volume = "22",
number = "5",
pages = "786--802",
month = may,
year = "2011",
CODEN = "ITDSEO",
DOI = "https://doi.org/10.1109/TPDS.2010.119",
ISSN = "1045-9219 (print), 1558-2183 (electronic)",
ISSN-L = "1045-9219",
bibdate = "Fri Jun 3 12:50:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5482570",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=71",
fjournal = "IEEE Transactions on Parallel and Distributed
Systems",
journal-URL = "http://www.computer.org/tpds/archives.htm",
}
@Article{Chakrabarti:2011:IDQ,
author = "Soumen Chakrabarti and Amit Pathak and Manish Gupta",
title = "Index design and query processing for graph
conductance search",
journal = j-VLDB-J,
volume = "20",
number = "3",
pages = "445--470",
month = jun,
year = "2011",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-010-0204-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Tue Jun 14 11:27:46 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "Graph conductance queries, also known as personalized
PageRank and related to random walks with restarts,
were originally proposed to assign a hyperlink-based
prestige score to Web pages. More general forms of such
queries are also very useful for ranking in
entity-relation (ER) graphs used to represent
relational, XML and hypertext data. Evaluation of
PageRank usually involves a global eigen computation.
If the graph is even moderately large, interactive
response times may not be possible. Recently, the need
for interactive PageRank evaluation has increased. The
graph may be fully known only when the query is
submitted. Browsing actions of the user may change some
inputs to the PageRank computation dynamically.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Chung:2011:DPT,
author = "Fan Chung and Alexander Tsiatas and Wensong Xu",
editor = "Alan Frieze and Paul Horn and Pawe{\l} Pra{\l}at",
booktitle = "{Algorithms and Models for the Web Graph: 8th
International Workshop, WAW 2011, Atlanta, GA, USA, May
27--29, 2011. Proceedings}",
title = "{Dirichlet PageRank} and trust-based ranking
algorithms",
journal = j-LECT-NOTES-COMP-SCI,
volume = "6732",
pages = "103--114",
year = "2011",
CODEN = "LNCSD9",
DOI = "https://doi.org/10.1007/978-3-642-21286-4_9",
ISBN = "3-642-21285-9 (print), 3-642-21286-7 (electronic)",
ISBN-13 = "978-3-642-21285-7 (print), 978-3-642-21286-4
(electronic)",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
bibdate = "Tue Aug 11 16:30:03 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
book-URL = "http://link.springer.com/book/10.1007/978-3-540-95995-3",
fjournal = "Lecture Notes in Computer Science",
journal-URL = "http://link.springer.com/bookseries/558",
}
@Article{Dayar:2011:SSA,
author = "Tugrul Dayar and G{\"o}k{\c{c}}e N. Noyan",
title = "Steady-state analysis of {Google}-like stochastic
matrices with block iterative methods",
journal = j-ELECTRON-TRANS-NUMER-ANAL,
volume = "38",
pages = "69--97",
year = "2011",
CODEN = "????",
ISSN = "1068-9613 (print), 1097-4067 (electronic)",
ISSN-L = "1068-9613",
bibdate = "Thu Jun 9 12:14:22 MDT 2011",
bibsource = "http://etna.mcs.kent.edu/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "A Google-like matrix is a positive stochastic matrix
given by a convex combination of a sparse, nonnegative
matrix and a particular rank one matrix. Google itself
uses the steady-state vector of a large matrix of this
form to help order web pages in a search engine. We
investigate the computation of the steady-state vectors
of such matrices using block iterative methods. The
block partitionings considered include those based on
block triangular form and those having triangular
diagonal blocks obtained using cutsets. Numerical
results show that block Gauss-Seidel with partitionings
based on block triangular form is most often the best
approach. However, there are cases in which a block
partitioning with triangular diagonal blocks is better,
and the Gauss-Seidel method is usually competitive.",
acknowledgement = ack-nhfb,
fjournal = "Electronic Transactions on Numerical Analysis",
keywords = "block iterative methods; cutsets; Google; PageRank;
partitionings; power method; stochastic matrices;
triangular blocks",
}
@Article{Ding:2011:AWP,
author = "Ying Ding",
title = "Applying weighted {PageRank} to author citation
networks",
journal = j-J-AM-SOC-INF-SCI-TECHNOL,
volume = "62",
number = "2",
pages = "236--245",
month = feb,
year = "2011",
CODEN = "JASIEF",
DOI = "https://doi.org/10.1002/asi.21452",
ISSN = "1532-2882 (print), 1532-2890 (electronic)",
ISSN-L = "1532-2882",
bibdate = "Fri Sep 11 10:43:05 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jasist.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Journal of the American Society for Information
Science and Technology: JASIST",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890",
onlinedate = "15 Nov 2010",
}
@Article{Ding:2011:TBP,
author = "Ying Ding",
title = "Topic-based {PageRank} on author cocitation networks",
journal = j-J-AM-SOC-INF-SCI-TECHNOL,
volume = "62",
number = "3",
pages = "449--466",
month = mar,
year = "2011",
CODEN = "JASIEF",
DOI = "https://doi.org/10.1002/asi.21467",
ISSN = "1532-2882 (print), 1532-2890 (electronic)",
ISSN-L = "1532-2882",
bibdate = "Fri Sep 11 10:43:06 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jasist.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Journal of the American Society for Information
Science and Technology: JASIST",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890",
onlinedate = "18 Jan 2011",
}
@Article{Frahm:2011:UEP,
author = "K. M. Frahm and .B Georgeot and D. L. Shepelyansky",
title = "Universal emergence of {PageRank}",
journal = j-J-PHYS-A-MATH-THEOR,
volume = "44",
number = "46",
pages = "465101:1--465101:17",
day = "18",
month = nov,
year = "2011",
CODEN = "JPAMB5",
DOI = "https://doi.org/10.1088/1751-8113/44/46/465101",
ISSN = "1751-8113 (print), 1751-8121 (electronic)",
ISSN-L = "1751-8113",
bibdate = "Wed Aug 12 08:26:23 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://stacks.iop.org/1751-8121/44/i=46/a=465101",
abstract = "The PageRank algorithm enables us to rank the nodes of
a network through a specific eigenvector of the Google
matrix, using a damping parameter $]0, 1 [$. Using
extensive numerical simulations of large web networks,
with a special accent on British University networks,
we determine numerically and analytically the universal
features of the PageRank vector at its emergence when
1. The whole network can be divided into a core part
and a group of invariant subspaces. For 1, PageRank
converges to a universal power-law distribution on the
invariant subspaces whose size distribution also
follows a universal power law. The convergence of
PageRank at 1 is controlled by eigenvalues of the core
part of the Google matrix, which are extremely close to
unity, leading to large relaxation times as, for
example, in spin glasses.",
acknowledgement = ack-nhfb,
fjournal = "Journal of Physics A: Mathematical and Theoretical",
journal-URL = "http://iopscience.iop.org/1751-8121",
}
@Article{Franceschet:2011:PSS,
author = "Massimo Franceschet",
title = "{PageRank}: standing on the shoulders of giants",
journal = j-CACM,
volume = "54",
number = "6",
pages = "92--101",
month = jun,
year = "2011",
CODEN = "CACMA2",
DOI = "https://doi.org/10.1145/1953122.1953146",
ISSN = "0001-0782 (print), 1557-7317 (electronic)",
ISSN-L = "0001-0782",
bibdate = "Wed Jun 1 18:12:20 MDT 2011",
bibsource = "http://www.acm.org/pubs/contents/journals/cacm/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "The roots of Google's PageRank can be traced back to
several early, and equally remarkable, ranking
techniques.",
acknowledgement = ack-nhfb,
fjournal = "Communications of the ACM",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J79",
}
@Article{Greif:2011:NCS,
author = "Chen Greif and David Kurokawa",
title = "A Note on the Convergence of {SOR} for the {PageRank}
Problem",
journal = j-SIAM-J-SCI-COMP,
volume = "33",
number = "6",
pages = "3201--3209",
month = "????",
year = "2011",
CODEN = "SJOCE3",
DOI = "https://doi.org/10.1137/110823523",
ISSN = "1064-8275 (print), 1095-7197 (electronic)",
ISSN-L = "1064-8275",
bibdate = "Thu Feb 9 06:05:59 MST 2012",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SISC/33/6;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib",
URL = "http://epubs.siam.org/sisc/resource/1/sjoce3/v33/i6/p3201_s1",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Scientific Computing",
journal-URL = "http://epubs.siam.org/sisc",
onlinedate = "November 08, 2011",
}
@InProceedings{Keong:2011:PMR,
author = "Boo Vooi Keong and Patricia Anthony",
booktitle = "{2011 7th International Conference on Information
Technology in Asia (CITA 11)}",
title = "{PageRank}: a modified random surfer model",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--6",
year = "2011",
DOI = "https://doi.org/10.1109/CITA.2011.5998269",
bibdate = "Mon Sep 12 21:28:08 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5998269",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5984722",
}
@Article{Levene:2011:BRS,
author = "Mark Levene",
title = "Book Review: {Search Engines: Information Retrieval in
Practice}",
journal = j-COMP-J,
volume = "54",
number = "5",
pages = "831--832",
month = may,
year = "2011",
CODEN = "CMPJA6",
DOI = "https://doi.org/10.1093/comjnl/bxq039",
ISSN = "0010-4620 (print), 1460-2067 (electronic)",
ISSN-L = "0010-4620",
bibdate = "Thu May 5 19:16:16 MDT 2011",
bibsource = "content/54/5.toc;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "See \cite{Croft:2010:SEI}.",
URL = "http://comjnl.oxfordjournals.org/content/54/5/831.full.pdf+html",
acknowledgement = ack-nhfb,
fjournal = "The Computer Journal",
journal-URL = "http://comjnl.oxfordjournals.org/",
keywords = "Google; PageRank",
onlinedate = "April 13, 2010",
}
@Book{Levy:2011:PHG,
author = "Steven Levy",
title = "In the plex: how {Google} thinks, works, and shapes
our lives",
publisher = "Simon and Schuster",
address = "New York, NY, USA",
pages = "v + 424",
year = "2011",
ISBN = "1-4165-9658-5",
ISBN-13 = "978-1-4165-9658-5",
LCCN = "HD9696.8.U64 G6657 2011",
bibdate = "Fri Jun 3 09:45:37 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
abstract = "Written with full cooperation from top management at
Google, this is the story behind the most successful
and admired technology company of our time.",
acknowledgement = ack-nhfb,
subject = "Google; Internet industry; United States",
tableofcontents = "The world according to Google: biography of a
search engine \\
Googlenomics: cracking the code on Internet profits \\
Don't be evil: how Google built its culture \\
Google's cloud: how Google built data centers and
killed the hard drive \\
Outside the box: the Google phone company. and the
Google t.v. company \\
Guge: Google moral dilemma in China \\
Google.gov: is what's good for Google, good for
government or the public? \\
Epilogue: chasing tail lights: trying to crack the
social code",
}
@Article{Menon:2011:FAA,
author = "Aditya Krishna Menon and Charles Elkan",
title = "Fast Algorithms for Approximating the Singular Value
Decomposition",
journal = j-TKDD,
volume = "5",
number = "2",
pages = "13:1--13:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1921632.1921639",
ISSN = "1556-4681 (print), 1556-472X (electronic)",
ISSN-L = "1556-4681",
bibdate = "Mon Mar 28 11:44:01 MDT 2011",
bibsource = "http://www.acm.org/pubs/contents/journals/tkdd/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "A low-rank approximation to a matrix $A$ is a matrix
with significantly smaller rank than $A$, and which is
close to $A$ according to some norm. Many practical
applications involving the use of large matrices focus
on low-rank approximations. By reducing the rank or
dimensionality of the data, we reduce the complexity of
analyzing the data. The singular value decomposition is
the most popular low-rank matrix approximation.
However, due to its expensive computational
requirements, it has often been considered intractable
for practical applications involving massive data.
Recent developments have tried to address this problem,
with several methods proposed to approximate the
decomposition with better asymptotic runtime. We
present an empirical study of these techniques on a
variety of dense and sparse datasets. We find that a
sampling approach of Drineas, Kannan and Mahoney is
often, but not always, the best performing method. This
method gives solutions with high accuracy much faster
than classical SVD algorithms, on large sparse datasets
in particular. Other modern methods, such as a recent
algorithm by Rokhlin and Tygert, also offer savings
compared to classical SVD algorithms. The older
sampling methods of Achlioptas and McSherry are shown
to sometimes take longer than classical SVD.",
acknowledgement = ack-nhfb,
articleno = "13",
fjournal = "ACM Transactions on Knowledge Discovery from Data
(TKDD)",
}
@Article{Sarma:2011:EPG,
author = "Atish Das Sarma and Sreenivas Gollapudi and Rina
Panigrahy",
title = "Estimating {PageRank} on graph streams",
journal = j-J-ACM,
volume = "58",
number = "3",
pages = "13:1--13:19",
month = may,
year = "2011",
CODEN = "JACOAH",
DOI = "https://doi.org/10.1145/1970392.1970397",
ISSN = "0004-5411 (print), 1557-735X (electronic)",
ISSN-L = "0004-5411",
bibdate = "Fri Jun 3 18:12:24 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "This article focuses on computations on large graphs
(e.g., the web-graph) where the edges of the graph are
presented as a stream. The objective in the streaming
model is to use small amount of memory (preferably
sub-linear in the number of nodes $n$) and a smaller
number of passes.\par In the streaming model, we show
how to perform several graph computations including
estimating the probability distribution after a random
walk of length $l$, the mixing time $M$, and other
related quantities such as the conductance of the
graph. By applying our algorithm for computing
probability distribution on the web-graph, we can
estimate the PageRank $p$ of any node up to an additive
error of $ \sqrt {\epsilon p} + \epsilon $ in $ {\~
O}(\sqrt {M / \alpha })$ passes and $ {\~ O}(\min (n
\alpha + 1 / \epsilon \sqrt {M / \alpha } + (1 /
\epsilon) M \alpha, \alpha n \sqrt {M \alpha } + (1 /
\epsilon) \sqrt {M / \alpha }))$ space, for any $
\alpha \in (0, 1]$. Specifically, for $ \epsilon = M /
n$, $ \alpha = M^{-1 / 2}$, we can compute the
approximate PageRank values in $ {\~ O}(n M^{-1 / 4})$
space and $ {\~ O}(^M^{3 / 4})$ passes. In comparison,
a standard implementation of the PageRank algorithm
will take $ O(n)$ space and $ O(M)$ passes. We also
give an approach to approximate the PageRank values in
just $ {\~ O}(1)$ passes although this requires $ {\~
O}(n M)$ space.",
acknowledgement = ack-nhfb,
articleno = "13",
fjournal = "Journal of the ACM",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J401",
}
@InProceedings{Shen:2011:PAM,
author = "Xiaowei Shen and Xiwei Liu and Dong Fan and Changjian
Cheng and Gang Xiong",
booktitle = "{2011 IEEE International Conference on Service
Operations, Logistics, and Informatics (SOLI)}",
title = "A performance appraisal method based on {ACP} theory
and {PageRank} algorithm",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "197--201",
year = "2011",
DOI = "https://doi.org/10.1109/SOLI.2011.5986555",
bibdate = "Mon Sep 12 21:28:08 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5986555",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5975308",
}
@Article{Tudisco:2011:PAP,
author = "Francesco Tudisco and Carmine {Di Fiore}",
title = "A preconditioning approach to the pagerank computation
problem",
journal = j-LINEAR-ALGEBRA-APPL,
volume = "435",
number = "9",
pages = "2222--2246",
day = "1",
month = nov,
year = "2011",
CODEN = "LAAPAW",
DOI = "https://doi.org/10.1016/j.laa.2011.04.018",
ISSN = "0024-3795 (print), 1873-1856 (electronic)",
ISSN-L = "0024-3795",
bibdate = "Mon Jun 13 18:34:49 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/linala2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/00243795",
acknowledgement = ack-nhfb,
fjournal = "Linear Algebra and its Applications",
journal-URL = "http://www.sciencedirect.com/science/journal/00243795",
}
@Article{Yan:2011:FBA,
author = "Jing Yan and Ning-Yi Xu and Xiong-Fei Cai and Rui Gao
and Yu Wang and Rong Luo and Feng-Hsiung Hsu",
title = "An {FPGA}-based accelerator for {LambdaRank} in Web
search engines",
journal = j-TRETS,
volume = "4",
number = "3",
pages = "25:1--25:??",
month = aug,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2000832.2000837",
ISSN = "1936-7406 (print), 1936-7414 (electronic)",
ISSN-L = "1936-7406",
bibdate = "Tue Aug 30 08:13:57 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "In modern Web search engines, Neural Network
(NN)-based learning to rank algorithms is intensively
used to increase the quality of search results.
LambdaRank is one such algorithm. However, it is hard
to be efficiently accelerated by computer clusters or
GPUs, because: (i) the cost function for the ranking
problem is much more complex than that of traditional
Back-Propagation(BP) NNs, and (ii) no coarse-grained
parallelism exists in the algorithm. This article
presents an FPGA-based accelerator solution to provide
high computing performance with low power consumption.
A compact deep pipeline is proposed to handle the
complex computing in the batch updating. The area
scales linearly with the number of hidden nodes in the
algorithm. We also carefully design a data format to
enable streaming consumption of the training data from
the host computer. The accelerator shows up to 15.3X
(with PCIe x4) and 23.9X (with PCIe x8) speedup
compared with the pure software implementation on
datasets from a commercial search engine.",
acknowledgement = ack-nhfb,
articleno = "25",
fjournal = "ACM Transactions on Reconfigurable Technology and
Systems (TRETS)",
journal-URL = "http://portal.acm.org/toc.cfm?id=J1151",
}
@InProceedings{Yan:2011:RPH,
author = "Lili Yan and Yingbin Wei and Zhanji Gui and Yizhuo
Chen",
booktitle = "{2011 International Conference on Internet Technology
and Applications (iTAP)}",
title = "Research on {PageRank} and Hyperlink-Induced Topic
Search in {Web} Structure Mining",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--4",
year = "2011",
DOI = "https://doi.org/10.1109/ITAP.2011.6006308",
bibdate = "Mon Sep 12 21:28:08 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6006308",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6005185",
}
@InProceedings{Zha:2011:EIS,
author = "Peng Zha and Xiu Xu and Ming Zuo",
booktitle = "{2011 International Conference on Management and
Service Science (MASS)}",
title = "An Efficient Improved Strategy for the {PageRank}
Algorithm",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--4",
year = "2011",
DOI = "https://doi.org/10.1109/ICMSS.2011.5999297",
bibdate = "Mon Sep 12 21:28:08 MDT 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5999297",
acknowledgement = ack-nhfb,
book-URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5996071",
}
@Article{Agryzkov:2012:ARN,
author = "Taras Agryzkov and Jose L. Oliver and Leandro Tortosa
and Jose F. Vicent",
title = "An algorithm for ranking the nodes of an urban network
based on the concept of {PageRank} vector",
journal = j-APPL-MATH-COMP,
volume = "219",
number = "4",
pages = "2186--2193",
day = "1",
month = nov,
year = "2012",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2012.08.064",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Thu Oct 25 09:05:21 MDT 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/00963003",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300312008570",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003",
}
@Article{Bai:2012:CIO,
author = "Zhong-Zhi Bai",
title = "On convergence of the inner--outer iteration method
for computing {PageRank}",
journal = j-NUMER-ALGEBRA-CONTROL-OPTIM,
volume = "2",
number = "4",
pages = "855--862",
month = "????",
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.3934/naco.2012.2.855",
ISSN = "2155-3289 (print), 2155-3297 (electronic)",
ISSN-L = "2155-3297",
bibdate = "Thu Jan 31 08:21:10 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/naco.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://aimsciences.org/article/doi/10.3934/naco.2012.2.855",
acknowledgement = ack-nhfb,
ajournal = "Numer. Algebra Control Optim.",
fjournal = "Numerical Algebra, Control and Optimization",
journal-URL = "http://aimsciences.org/journal/2155-3289",
}
@Article{Borgs:2012:STA,
author = "Christian Borgs and Michael Brautbar",
title = "A Sublinear Time Algorithm for {PageRank}
Computations",
journal = j-LECT-NOTES-COMP-SCI,
volume = "7323",
pages = "41--53",
year = "2012",
CODEN = "LNCSD9",
DOI = "https://doi.org/10.1007/978-3-642-30541-2_4",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
bibdate = "Mon Dec 24 07:30:37 MST 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/accesspage/chapter/10.1007/978-3-642-30541-2_3;
http://link.springer.com/chapter/10.1007/978-3-642-30541-2_4/;
http://link.springer.com/content/pdf/10.1007/978-3-642-30541-2_4",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1007/978-3-642-30541-2",
book-URL = "http://www.springerlink.com/content/978-3-642-30541-2",
fjournal = "Lecture Notes in Computer Science",
}
@Article{Brin:2012:RAL,
author = "Sergey Brin and Lawrence Page",
title = "Reprint of: {The anatomy of a large-scale hypertextual
Web search engine}",
journal = j-COMP-NET-AMSTERDAM,
volume = "56",
number = "18",
pages = "3825--3833",
day = "17",
month = dec,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.comnet.2012.10.007",
ISSN = "1389-1286 (print), 1872-7069 (electronic)",
ISSN-L = "1389-1286",
bibdate = "Fri Nov 30 12:26:39 MST 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compnetamsterdam2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/13891286",
URL = "http://www.sciencedirect.com/science/article/pii/S1389128612003611",
acknowledgement = ack-nhfb,
fjournal = "Computer Networks",
journal-URL = "http://www.sciencedirect.com/science/journal/13891286",
keywords = "PageRank algorithm",
}
@Article{Chung:2012:MCA,
author = "Fan Chung and Paul Horn and Jacob Hughes",
title = "Multi-commodity Allocation for Dynamic Demands Using
{PageRank} Vectors",
journal = j-LECT-NOTES-COMP-SCI,
volume = "7323",
pages = "138--152",
year = "2012",
CODEN = "LNCSD9",
DOI = "https://doi.org/10.1007/978-3-642-30541-2_11",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
bibdate = "Mon Dec 24 07:30:37 MST 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/content/pdf/10.1007/978-3-642-30541-2_11",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1007/978-3-642-30541-2",
book-URL = "http://www.springerlink.com/content/978-3-642-30541-2",
fjournal = "Lecture Notes in Computer Science",
}
@Article{Fiala:2012:TAP,
author = "Dalibor Fiala",
title = "Time-aware {PageRank} for bibliographic networks",
journal = j-J-INFORMETRICS,
volume = "6",
number = "3",
pages = "370--388",
month = jul,
year = "2012",
CODEN = "????",
ISSN = "1751-1577 (print), 1875-5879 (electronic)",
ISSN-L = "1751-1577",
bibdate = "Wed Sep 9 16:29:46 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S1751157712000119",
acknowledgement = ack-nhfb,
fjournal = "Journal of Informetrics",
journal-URL = "http://www.sciencedirect.com/science/journal/17511577/",
}
@Article{Frahm:2012:PI,
author = "K. M. Frahm and A. D. Chepelianskii and D. L.
Shepelyansky",
title = "{PageRank} of integers",
journal = j-J-PHYS-A-MATH-THEOR,
volume = "45",
number = "40",
pages = "405101:1--405101:20",
day = "12",
month = oct,
year = "2012",
CODEN = "JPAMB5",
DOI = "https://doi.org/10.1088/1751-8113/45/40/405101",
ISSN = "1751-8113 (print), 1751-8121 (electronic)",
ISSN-L = "1751-8113",
bibdate = "Wed Aug 12 08:11:49 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://iopscience.iop.org/1751-8121/45/40/405101",
acknowledgement = ack-nhfb,
fjournal = "Journal of Physics A (Mathematical and General)",
journal-URL = "http://iopscience.iop.org/1751-8121",
}
@Article{Hudelson:2012:DPA,
author = "Matthew Hudelson and Barbara Logan Mooney and Aurora
E. Clark",
title = "Determining polyhedral arrangements of atoms using
{PageRank}",
journal = j-J-MATH-CHEM,
volume = "50",
number = "9",
pages = "2342--2350",
month = oct,
year = "2012",
CODEN = "JMCHEG",
DOI = "https://doi.org/10.1007/s10910-012-0033-7",
ISSN = "0259-9791 (print), 1572-8897 (electronic)",
ISSN-L = "0259-9791",
bibdate = "Thu Apr 9 18:14:24 MDT 2015",
bibsource = "http://link.springer.com/journal/10910/50/9;
https://www.math.utah.edu/pub/tex/bib/jmathchem.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/article/10.1007/s10910-012-0033-7",
acknowledgement = ack-nhfb,
fjournal = "Journal of Mathematical Chemistry",
journal-URL = "http://link.springer.com/journal/10910",
journalabr = "J. Math. Chem.",
}
@Article{Kumar:2012:PPM,
author = "Tarun Kumar and Parikshit Sondhi and Ankush Mittal",
title = "Parallelization of {PageRank} on Multicore
Processors",
journal = j-LECT-NOTES-COMP-SCI,
volume = "7154",
pages = "129--140",
year = "2012",
CODEN = "LNCSD9",
DOI = "https://doi.org/10.1007/978-3-642-28073-3_12",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
bibdate = "Mon Dec 24 07:16:06 MST 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/lncs2012b.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/content/pdf/10.1007/978-3-642-28073-3_12",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1007/978-3-642-28073-3",
book-URL = "http://www.springerlink.com/content/978-3-642-28073-3",
fjournal = "Lecture Notes in Computer Science",
}
@Book{Langville:2012:WNO,
author = "Amy N. Langville and C. D. (Carl Dean) Meyer",
title = "Who's number one?: the science of rating and ranking",
publisher = pub-PRINCETON,
address = pub-PRINCETON:adr,
pages = "xvi + 247",
year = "2012",
ISBN = "0-691-15422-8",
ISBN-13 = "978-0-691-15422-0",
LCCN = "QA278.75 .L36 2012",
bibdate = "Tue Aug 11 17:18:26 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
acknowledgement = ack-nhfb,
subject = "Ranking and selection (Statistics)",
tableofcontents = "Introduction to ranking \\
Massey's method \\
Colley's method \\
Keener's method \\
Elo's system \\
The Markov method \\
The offense-defense rating method \\
Ranking by reordering methods \\
Point spreads \\
User preference ratings \\
Handling ties \\
Incorporating weights \\
``What if'' scenarios and sensitivity \\
Rank aggregation: part 1 \\
Rank aggregation: part 2 \\
Methods of comparison \\
Data \\
Epilogue",
xxtitle = "Who's \#1?: the science of rating and ranking",
}
@Article{Liu:2012:IPA,
author = "Dian-Xing Liu and Xia Yan and Wei Xie",
title = "Improved {PageRank} Algorithm Based on the Residence
Time of the {Website}",
journal = j-LECT-NOTES-COMP-SCI,
volume = "7390",
pages = "601--607",
year = "2012",
CODEN = "LNCSD9",
DOI = "https://doi.org/10.1007/978-3-642-31576-3_76",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
bibdate = "Mon Dec 24 07:42:40 MST 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/lncs2012f.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/content/pdf/10.1007/978-3-642-31576-3_76",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1007/978-3-642-31576-3",
book-URL = "http://www.springerlink.com/content/978-3-642-31576-3",
fjournal = "Lecture Notes in Computer Science",
}
@Book{MacCormick:2012:NAC,
author = "John MacCormick",
title = "Nine Algorithms That Changed the Future: the Ingenious
Ideas That Drive Today's Computers",
publisher = pub-PRINCETON,
address = pub-PRINCETON:adr,
pages = "x + 2 + 219",
year = "2012",
ISBN = "0-691-14714-0 (hardcover), 0-691-15819-3 (paperback)",
ISBN-13 = "978-0-691-14714-7 (hardcover), 978-0-691-15819-8
(paperback)",
LCCN = "QA76 .M21453 2012",
bibdate = "Tue May 5 17:16:06 MDT 2015",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/mathgaz2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
note = "With a foreword by Christopher M. Bishop.",
URL = "http://press.princeton.edu/chapters/s9528.pdf;
http://www.jstor.org/stable/10.2307/j.ctt7t71s",
abstract = "Every day, we use our computers to perform remarkable
feats. A simple web search picks out a handful of
relevant needles from the world's biggest haystack: the
billions of pages on the World Wide Web. Uploading a
photo to Facebook transmits millions of pieces of
information over numerous error-prone network links,
yet somehow a perfect copy of the photo arrives intact.
Without even knowing it, we use public-key cryptography
to transmit secret information like credit card
numbers; and, we use digital signatures to verify the
identity of the websites we visit. How do our computers
perform these tasks with such ease?\par
This is the first book to answer that question in
language anyone can understand, revealing the
extraordinary ideas that power our PCs, laptops, and
smartphones. Using vivid examples, John MacCormick
explains the fundamental ``tricks'' behind nine types
of computer algorithms, including artificial
intelligence (where we learn about the ``nearest
neighbor trick'' and ``twenty questions trick''),
Google's famous PageRank algorithm (which uses the
``random surfer trick''), data compression, error
correction, and much more.\par
These revolutionary algorithms have changed our world:
this book unlocks their secrets, and lays bare the
incredible ideas that our computers use every day.",
acknowledgement = ack-nhfb,
author-dates = "1972--",
remark = "The coverage of the history of PageRank algorithm in
this book is deficient; see the commentary in
\cite{Robertson:2019:BHS}.",
subject = "Computer science; Computer algorithms; Artificial
intelligence",
tableofcontents = "Foreword / ix \\
1. Introduction: What Are the Extraordinary Ideas
Computers Use Every Day? / 1 \\
2. Search Engine Indexing: Finding Needles in the
World's Biggest Haystack / 10 \\
3. PageRank: The Technology That Launched Google / 24
\\
4. Public Key Cryptography: Sending Secrets on a
Postcard 38 \\
5. Error-Correcting Codes: Mistakes That Fix Themselves
/ 60 \\
6. Pattern Recognition: Learning from Experience / 80
\\
7. Data Compression: Something for Nothing / 105 \\
8. Databases: The Quest for Consistency / 122 \\
9. Digital Signatures: Who Really Wrote This Software?
/ 149 \\
10. What Is Computable? / 174 \\
11. Conclusion: More Genius at Your Fingertips? / 199
\\
Acknowledgments / 205 \\
Sources and Further Reading / 207 \\
Index / 211",
}
@Article{Makris:2012:WQD,
author = "Christos Makris and Yannis Plegas and Sofia Stamou",
title = "{Web} query disambiguation using {PageRank}",
journal = j-J-AM-SOC-INF-SCI-TECHNOL,
volume = "63",
number = "8",
pages = "1581--1592",
month = aug,
year = "2012",
CODEN = "JASIEF",
DOI = "https://doi.org/10.1002/asi.22685",
ISSN = "1532-2882 (print), 1532-2890 (electronic)",
ISSN-L = "1532-2882",
bibdate = "Fri Sep 11 10:43:15 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jasist.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Journal of the American Society for Information
Science and Technology: JASIST",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890",
onlinedate = "29 Jun 2012",
}
@InCollection{Rebaza:2012:GPA,
author = "Jorge Rebaza",
title = "{Google}'s {PageRank} Algorithm",
crossref = "Rebaza:2012:FCA",
chapter = "2.3",
pages = "??--??",
year = "2012",
bibdate = "Tue May 12 09:32:37 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
}
@Article{Rossi:2012:DPU,
author = "Ryan A. Rossi and David F. Gleich",
title = "Dynamic {PageRank} Using Evolving Teleportation",
journal = j-LECT-NOTES-COMP-SCI,
volume = "7323",
pages = "126--137",
year = "2012",
CODEN = "LNCSD9",
DOI = "https://doi.org/10.1007/978-3-642-30541-2_10",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
bibdate = "Mon Dec 24 07:30:37 MST 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/content/pdf/10.1007/978-3-642-30541-2_10",
acknowledgement = ack-nhfb,
book-DOI = "https://doi.org/10.1007/978-3-642-30541-2",
book-URL = "http://www.springerlink.com/content/978-3-642-30541-2",
fjournal = "Lecture Notes in Computer Science",
}
@Article{Sanderson:2012:HIR,
author = "M. Sanderson and W. B. Croft",
title = "The History of Information Retrieval Research",
journal = j-PROC-IEEE,
volume = "100",
number = "Special Centennial Issue",
pages = "1444--1451",
month = may,
year = "2012",
CODEN = "IEEPAD",
DOI = "https://doi.org/10.1109/jproc.2012.2189916",
ISSN = "0018-9219 (print), 1558-2256 (electronic)",
ISSN-L = "0018-9219",
bibdate = "Mon Jul 8 08:40:26 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the IEEE",
journal-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5",
}
@Article{Winter:2012:GGC,
author = "Christof Winter and Glen Kristiansen and Stephan
Kersting and Janine Roy and Daniela Aust and Thomas
Kn{\"o}sel and Petra R{\"u}mmele and Beatrix Jahnke and
Vera Hentrich and Felix R{\"u}ckert and Marco
Niedergethmann and Wilko Weichert and Marcus Bahra and
Hans J. Schlitt and Utz Settmacher and Helmut Friess
and Markus B{\"u}chler and Hans-Detlev Saeger and
Michael Schroeder and Christian Pilarsky and Robert
Gr{\"u}tzmann",
title = "{Google} goes cancer: Improving outcome prediction for
cancer patients by network-based ranking of marker
genes",
journal = j-PLOS-COMPUT-BIOL,
volume = "8",
number = "??",
pages = "e1002511",
month = jul,
year = "2012",
CODEN = "PCBLBG",
DOI = "https://doi.org/10.1371/journal.pcbi.1002511",
ISSN = "1553-734X (print), 1553-7358 (electronic)",
ISSN-L = "1553-734X",
bibdate = "Tue Aug 11 17:47:14 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002511",
abstract = "Predicting the clinical outcome of cancer patients
based on the expression of marker genes in their tumors
has received increasing interest in the past decade.
Accurate predictors of outcome and response to therapy
could be used to personalize and thereby improve
therapy. However, state of the art methods used so far
often found marker genes with limited prediction
accuracy, limited reproducibility, and unclear
biological relevance. To address this problem, we
developed a novel computational approach to identify
genes prognostic for outcome that couples gene
expression measurements from primary tumor samples with
a network of known relationships between the genes. Our
approach ranks genes according to their prognostic
relevance using both expression and network information
in a manner similar to Google's PageRank. We applied
this method to gene expression profiles which we
obtained from 30 patients with pancreatic cancer, and
identified seven candidate marker genes prognostic for
outcome. Compared to genes found with state of the art
methods, such as Pearson correlation of gene expression
with survival time, we improve the prediction accuracy
by up to 7\%. Accuracies were assessed using support
vector machine classifiers and Monte Carlo
cross-validation. We then validated the prognostic
value of our seven candidate markers using
immunohistochemistry on an independent set of 412
pancreatic cancer samples. Notably, signatures derived
from our candidate markers were independently
predictive of outcome and superior to established
clinical prognostic factors such as grade, tumor size,
and nodal status. As the amount of genomic data of
individual tumors grows rapidly, our algorithm meets
the need for powerful computational approaches that are
key to exploit these data for personalized cancer
therapies in clinical practice.",
acknowledgement = ack-nhfb,
fjournal = "PLoS Computational Biology",
journal-URL = "http://compbiol.plosjournals.org/",
keywords = "PageRank",
onlinedate = "17 May 2012",
}
@Article{Wu:2012:PSG,
author = "Gang Wu and Yan-Chun Wang and Xiao-Qing Jin",
title = "A Preconditioned and Shifted {GMRES} Algorithm for the
{PageRank} Problem with Multiple Damping Factors",
journal = j-SIAM-J-SCI-COMP,
volume = "34",
number = "5",
pages = "A2558--A2575",
month = "????",
year = "2012",
CODEN = "SJOCE3",
DOI = "https://doi.org/10.1137/110834585",
ISSN = "1064-8275 (print), 1095-7197 (electronic)",
ISSN-L = "1064-8275",
bibdate = "Tue Oct 30 14:49:10 MDT 2012",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SISC/34/5;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Scientific Computing",
journal-URL = "http://epubs.siam.org/sisc",
onlinedate = "January 2012",
}
@Article{Yin:2012:AAA,
author = "Jun-Feng Yin and Guo-Jian Yin and Michael Ng",
title = "On adaptively accelerated {Arnoldi} method for
computing {PageRank}",
journal = j-NUM-LIN-ALG-APPL,
volume = "19",
number = "1",
pages = "73--85",
month = jan,
year = "2012",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.789",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
ISSN-L = "1070-5325",
bibdate = "Fri Mar 16 18:11:23 MDT 2012",
bibsource = "http://www.interscience.wiley.com/jpages/1070-5325;
https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www3.interscience.wiley.com/journalfinder.html",
acknowledgement = ack-nhfb,
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506",
onlinedate = "20 Nov 2011",
}
@Article{Zhang:2012:AKR,
author = "Weinan Zhang and Dingquan Wang and Gui-Rong Xue and
Hongyuan Zha",
title = "Advertising Keywords Recommendation for Short-Text
{Web} Pages Using {Wikipedia}",
journal = j-TIST,
volume = "3",
number = "2",
pages = "36:1--36:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089112",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Advertising keywords recommendation is an
indispensable component for online advertising with the
keywords selected from the target Web pages used for
contextual advertising or sponsored search. Several
ranking-based algorithms have been proposed for
recommending advertising keywords. However, for most of
them performance is still lacking, especially when
dealing with short-text target Web pages, that is,
those containing insufficient textual information for
ranking. In some cases, short-text Web pages may not
even contain enough keywords for selection. A natural
alternative is then to recommend relevant keywords not
present in the target Web pages. In this article, we
propose a novel algorithm for advertising keywords
recommendation for short-text Web pages by leveraging
the contents of Wikipedia, a user-contributed online
encyclopedia. Wikipedia contains numerous entities with
related entities on a topic linked to each other. Given
a target Web page, we propose to use a content-biased
PageRank on the Wikipedia graph to rank the related
entities. Furthermore, in order to recommend
high-quality advertising keywords, we also add an
advertisement-biased factor into our model. With these
two biases, advertising keywords that are both relevant
to a target Web page and valuable for advertising are
recommended. In our experiments, several
state-of-the-art approaches for keyword recommendation
are compared. The experimental results demonstrate that
our proposed approach produces substantial improvement
in the precision of the top 20 recommended keywords on
short-text Web pages over existing approaches.",
acknowledgement = ack-nhfb,
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
}
@Article{Zhou:2012:PAC,
author = "Yunkai Zhou",
title = "Practical acceleration for computing the {HITS}
{ExpertRank} vectors",
journal = j-J-COMPUT-APPL-MATH,
volume = "236",
number = "17",
pages = "4398--4409",
month = nov,
year = "2012",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:24:36 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042712001665",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Banky:2013:EOL,
author = "D{\'a}niel B{\'a}nky and G{\'a}bor Iv{\'a}n and Vince
Grolmusz",
title = "Equal Opportunity for Low-Degree Network Nodes: A
{PageRank}-Based Method for Protein Target
Identification in Metabolic Graphs",
journal = j-PLOS-ONE,
volume = "8",
number = "1",
pages = "e54204:1--e54204:7",
month = jan,
year = "2013",
CODEN = "POLNCL",
DOI = "https://doi.org/10.1371/journal.pone.0054204",
ISSN = "1932-6203",
bibdate = "Wed Aug 12 08:33:35 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0054204",
abstract = "Biological network data, such as metabolic-,
signaling- or physical interaction graphs of proteins
are increasingly available in public repositories for
important species. Tools for the quantitative analysis
of these networks are being developed today. Protein
network-based drug target identification methods
usually return protein hubs with large degrees in the
networks as potentially important targets. Some known,
important protein targets, however, are not hubs at
all, and perturbing protein hubs in these networks may
have several unwanted physiological effects, due to
their interaction with numerous partners. Here, we show
a novel method applicable in networks with directed
edges (such as metabolic networks) that compensates for
the low degree (non-hub) vertices in the network, and
identifies important nodes, regardless of their hub
properties. Our method computes the PageRank for the
nodes of the network, and divides the PageRank by the
in-degree (i.e., the number of incoming edges) of the
node. This quotient is the same in all nodes in an
undirected graph (even for large- and low-degree nodes,
that is, for hubs and non-hubs as well), but may differ
significantly from node to node in directed graphs. We
suggest to assign importance to non-hub nodes with
large PageRank/in-degree quotient. Consequently, our
method gives high scores to nodes with large PageRank,
relative to their degrees: therefore non-hub important
nodes can easily be identified in large networks. We
demonstrate that these relatively high PageRank scores
have biological relevance: the method correctly finds
numerous already validated drug targets in distinct
organisms ({\em Mycobacterium tuberculosis}, {\em
Plasmodium falciparum\/} andd {\em MRSA Staphylococcus
aureus}), and consequently, it may suggest new possible
protein targets as well. Additionally, our scoring
method was not chosen arbitrarily: its value for all
nodes of all undirected graphs is constant; therefore
its high value captures importance in the directed edge
structure of the graph.",
acknowledgement = ack-nhfb,
fjournal = "PLoS One",
journal-URL = "http://www.plosone.org/",
}
@Article{Benzi:2013:CAG,
author = "Michele Benzi and Verena Kuhlemann",
title = "{Chebyshev} acceleration of the {GeneRank} algorithm",
journal = j-ELECTRON-TRANS-NUMER-ANAL,
volume = "40",
pages = "311--320",
year = "2013",
CODEN = "????",
ISSN = "1068-9613 (print), 1097-4067 (electronic)",
ISSN-L = "1068-9613",
bibdate = "Mon Mar 31 18:49:50 MDT 2014",
bibsource = "https://www.math.utah.edu/pub/tex/bib/etna.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://etna.mcs.kent.edu//vol.40.2013/pp311-320.dir/pp311-320.pdf",
acknowledgement = ack-nhfb,
journal-URL = "http://etna.mcs.kent.edu/",
}
@Article{Garcia:2013:LPP,
author = "E. Garc{\'\i}a and F. Pedroche and M. Romance",
title = "On the localization of the personalized {PageRank} of
complex networks",
journal = j-LINEAR-ALGEBRA-APPL,
volume = "439",
number = "3",
pages = "640--652",
day = "1",
month = aug,
year = "2013",
CODEN = "LAAPAW",
ISSN = "0024-3795 (print), 1873-1856 (electronic)",
ISSN-L = "0024-3795",
bibdate = "Mon Jun 24 07:02:58 MDT 2013",
bibsource = "https://www.math.utah.edu/pub/tex/bib/linala2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/00243795",
URL = "http://www.sciencedirect.com/science/article/pii/S0024379512007835",
acknowledgement = ack-nhfb,
fjournal = "Linear Algebra and its Applications",
journal-URL = "http://www.sciencedirect.com/science/journal/00243795",
}
@Article{Halu:2013:MP,
author = "Arda Halu and Ra{\'u}l J. Mondrag{\'o}n and Pietro
Panzarasa and Ginestra Bianconi",
title = "Multiplex {PageRank}",
journal = j-PLOS-ONE,
volume = "8",
number = "??",
pages = "e78293:1--e78293:10",
month = "????",
year = "2013",
CODEN = "POLNCL",
DOI = "https://doi.org/10.1371/journal.pone.0078293",
ISSN = "1932-6203",
bibdate = "Tue Aug 11 17:02:55 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0078293",
abstract = "Many complex systems can be described as multiplex
networks in which the same nodes can interact with one
another in different layers, thus forming a set of
interacting and co-evolving networks. Examples of such
multiplex systems are social networks where people are
involved in different types of relationships and
interact through various forms of communication media.
The ranking of nodes in multiplex networks is one of
the most pressing and challenging tasks that research
on complex networks is currently facing. When pairs of
nodes can be connected through multiple links and in
multiple layers, the ranking of nodes should
necessarily reflect the importance of nodes in one
layer as well as their importance in other
interdependent layers. In this paper, we draw on the
idea of biased random walks to define the Multiplex
PageRank centrality measure in which the effects of the
interplay between networks on the centrality of nodes
are directly taken into account. In particular,
depending on the intensity of the interaction between
layers, we define the Additive, Multiplicative,
Combined, and Neutral versions of Multiplex PageRank,
and show how each version reflects the extent to which
the importance of a node in one layer affects the
importance the node can gain in another layer. We
discuss these measures and apply them to an online
multiplex social network. Findings indicate that taking
the multiplex nature of the network into account helps
uncover the emergence of rankings of nodes that differ
from the rankings obtained from one single layer.
Results provide support in favor of the salience of
multiplex centrality measures, like Multiplex PageRank,
for assessing the prominence of nodes embedded in
multiple interacting networks, and for shedding a new
light on structural properties that would otherwise
remain undetected if each of the interacting networks
were analyzed in isolation.",
acknowledgement = ack-nhfb,
fjournal = "PLoS One",
journal-URL = "http://www.plosone.org/",
onlinedate = "30 October 2013",
}
@Article{Mcmillan:2013:PSR,
author = "Collin Mcmillan and Denys Poshyvanyk and Mark
Grechanik and Qing Xie and Chen Fu",
title = "{Portfolio}: Searching for relevant functions and
their usages in millions of lines of code",
journal = j-TOSEM,
volume = "22",
number = "4",
pages = "37:1--37:??",
month = oct,
year = "2013",
CODEN = "ATSMER",
DOI = "https://doi.org/10.1145/2522920.2522930",
ISSN = "1049-331X (print), 1557-7392 (electronic)",
ISSN-L = "1049-331X",
bibdate = "Wed Oct 30 12:18:03 MDT 2013",
bibsource = "http://www.acm.org/pubs/contents/journals/tosem/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tosem.bib",
abstract = "Different studies show that programmers are more
interested in finding definitions of functions and
their uses than variables, statements, or ordinary code
fragments. Therefore, developers require support in
finding relevant functions and determining how these
functions are used. Unfortunately, existing code search
engines do not provide enough of this support to
developers, thus reducing the effectiveness of code
reuse. We provide this support to programmers in a code
search system called Portfolio that retrieves and
visualizes relevant functions and their usages. We have
built Portfolio using a combination of models that
address surfing behavior of programmers and sharing
related concepts among functions. We conducted two
experiments: first, an experiment with 49 C/C++
programmers to compare Portfolio to Google Code Search
and Koders using a standard methodology for evaluating
information-retrieval-based engines; and second, an
experiment with 19 Java programmers to compare
Portfolio to Koders. The results show with strong
statistical significance that users find more relevant
functions with higher precision with Portfolio than
with Google Code Search and Koders. We also show that
by using PageRank, Portfolio is able to rank returned
relevant functions more efficiently.",
acknowledgement = ack-nhfb,
articleno = "37",
fjournal = "ACM Transactions on Software Engineering and
Methodology",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J790",
keywords = "PageRank algorithm",
}
@Article{Onizuka:2013:OIQ,
author = "Makoto Onizuka and Hiroyuki Kato and Soichiro Hidaka
and Keisuke Nakano and Zhenjiang Hu",
title = "Optimization for iterative queries on {MapReduce}",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "7",
number = "4",
pages = "241--252",
month = dec,
year = "2013",
CODEN = "????",
ISSN = "2150-8097",
bibdate = "Wed Feb 4 09:22:02 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
abstract = "We propose OptIQ, a query optimization approach for
iterative queries in distributed environment. OptIQ
removes redundant computations among different
iterations by extending the traditional techniques of
view materialization and incremental view evaluation.
First, OptIQ decomposes iterative queries into
invariant and variant views, and materializes the
former view. Redundant computations are removed by
reusing the materialized view among iterations. Second,
OptIQ incrementally evaluates the variant view, so that
redundant computations are removed by skipping the
evaluation on converged tuples in the variant view. We
verify the effectiveness of OptIQ through the queries
of PageRank and $k$-means clustering on real datasets.
The results show that OptIQ achieves high efficiency,
up to five times faster than is possible without
removing the redundant computations among iterations.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1174",
}
@InProceedings{Wang:2013:PPP,
author = "William Yang Wang and Kathryn Mazaitis and William W.
Cohen",
editor = "Qi He",
booktitle = "{CIKM'13: proceedings of the 22nd ACM International
Conference on Information and Knowledge Management:
Oct. 27--Nov. 1, 2013, San Francisco, CA, USA}",
title = "Programming with personalized {PageRank}: A locally
groundable first-order probabilistic logic",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "2129--2138",
year = "2013",
DOI = "https://doi.org/10.1145/2505515.2505573",
ISBN = "1-4503-2263-8",
ISBN-13 = "978-1-4503-2263-8",
LCCN = "QA76.9.D3",
bibdate = "Tue Aug 11 17:44:13 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
bookpages = "2574",
}
@Article{Wu:2013:AAT,
author = "Gang Wu and Ying Zhang and Yimin Wei",
title = "Accelerating the {Arnoldi}-Type Algorithm for the
{PageRank} Problem and the {ProteinRank} Problem",
journal = j-J-SCI-COMPUT,
volume = "57",
number = "1",
pages = "74--104",
month = oct,
year = "2013",
CODEN = "JSCOEB",
DOI = "https://doi.org/10.1007/s10915-013-9696-x",
ISSN = "0885-7474 (print), 1573-7691 (electronic)",
ISSN-L = "0885-7474",
bibdate = "Sat Mar 8 11:16:24 MST 2014",
bibsource = "http://link.springer.com/journal/10915;
http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0885-7474&volume=57&issue=1;
https://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/article/10.1007/s10915-013-9696-x;
http://link.springer.com/content/pdf/10.1007/s10915-013-9696-x.pdf",
acknowledgement = ack-nhfb,
fjournal = "Journal of Scientific Computing",
}
@Article{Zhang:2013:RWM,
author = "Zhu Zhang and Daniel D. Zeng and Ahmed Abbasi and Jing
Peng and Xiaolong Zheng",
title = "A Random Walk Model for Item Recommendation in Social
Tagging Systems",
journal = j-TMIS,
volume = "4",
number = "2",
pages = "8:1--8:??",
month = aug,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2490860",
ISSN = "2158-656X (print), 2158-6578 (electronic)",
ISSN-L = "2158-656X",
bibdate = "Thu Mar 13 06:54:56 MDT 2014",
bibsource = "http://www.acm.org/pubs/contents/journals/tmis/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tmis.bib",
abstract = "Social tagging, as a novel approach to information
organization and discovery, has been widely adopted in
many Web 2.0 applications. Tags contributed by users to
annotate a variety of Web resources or items provide a
new type of information that can be exploited by
recommender systems. Nevertheless, the sparsity of the
ternary interaction data among users, items, and tags
limits the performance of tag-based recommendation
algorithms. In this article, we propose to deal with
the sparsity problem in social tagging by applying
random walks on ternary interaction graphs to explore
transitive associations between users and items. The
transitive associations in this article refer to the
path of the link between any two nodes whose length is
greater than one. Taking advantage of these transitive
associations can allow more accurate measurement of the
relevance between two entities (e.g., user-item,
user-user, and item-item). A PageRank-like algorithm
has been developed to explore these transitive
associations by spreading users' preferences on an item
similarity graph and spreading items' influences on a
user similarity graph. Empirical evaluation on three
real-world datasets demonstrates that our approach can
effectively alleviate the sparsity problem and improve
the quality of item recommendation.",
acknowledgement = ack-nhfb,
articleno = "8",
fjournal = "ACM Transactions on Management Information Systems
(TMIS)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1320",
}
@Article{Zhu:2013:IAA,
author = "Fanwei Zhu and Yuan Fang and Kevin Chen-Chuan Chang
and Jing Ying",
title = "Incremental and accuracy-aware {Personalized PageRank}
through scheduled approximation",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "6",
number = "6",
pages = "481--492",
month = apr,
year = "2013",
CODEN = "????",
ISSN = "2150-8097",
bibdate = "Fri Dec 13 05:56:32 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
abstract = "As Personalized PageRank has been widely leveraged for
ranking on a graph, the efficient computation of
Personalized PageRank Vector (PPV) becomes a prominent
issue. In this paper, we propose FastPPV, an
approximate PPV computation algorithm that is
incremental and accuracy-aware. Our approach hinges on
a novel paradigm of scheduled approximation: the
computation is partitioned and scheduled for processing
in an ``organized'' way, such that we can gradually
improve our PPV estimation in an incremental manner,
and quantify the accuracy of our approximation at query
time. Guided by this principle, we develop an efficient
hub based realization, where we adopt the metric of
hub-length to partition and schedule random walk tours
so that the approximation error reduces exponentially
over iterations. Furthermore, as tours are segmented by
hubs, the shared substructures between different tours
(around the same hub) can be reused to speed up query
processing both within and across iterations. Finally,
we evaluate FastPPV over two real-world graphs, and
show that it not only significantly outperforms two
state-of-the-art baselines in both online and offline
phrases, but also scale well on larger graphs. In
particular, we are able to achieve near-constant time
online query processing irrespective of graph size.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
}
@Article{Amodio:2014:RAB,
author = "Pierluigi Amodio and Luigi Brugnano",
title = "Recent advances in bibliometric indexes and the
{PaperRank} problem",
journal = j-J-COMPUT-APPL-MATH,
volume = "267",
number = "??",
pages = "182--194",
month = sep,
year = "2014",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:34:44 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042714001046",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Arnal:2014:PRE,
author = "Josep Arnal and H{\'e}ctor Migall{\'o}n and Violeta
Migall{\'o}n",
title = "Parallel relaxed and extrapolated algorithms for
computing {PageRank}",
journal = j-J-SUPERCOMPUTING,
volume = "70",
number = "2",
pages = "637--648",
month = nov,
year = "2014",
CODEN = "JOSUED",
DOI = "https://doi.org/10.1007/s11227-014-1118-9",
ISSN = "0920-8542 (print), 1573-0484 (electronic)",
ISSN-L = "0920-8542",
bibdate = "Fri Feb 13 12:32:19 MST 2015",
bibsource = "http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0920-8542&volume=70&issue=2;
https://www.math.utah.edu/pub/tex/bib/jsuper.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/article/10.1007/s11227-014-1118-9",
acknowledgement = ack-nhfb,
fjournal = "The Journal of Supercomputing",
journal-URL = "http://link.springer.com/journal/11227",
}
@Article{Cheang:2014:MAE,
author = "Brenda Cheang and Samuel Kai Wah Chu and Chongshou Li
and Andrew Lim",
title = "A multidimensional approach to evaluating management
journals: {Refining} {PageRank} via the differentiation
of citation types and identifying the roles that
management journals play",
journal = j-J-ASSOC-INF-SCI-TECHNOL,
volume = "65",
number = "12",
pages = "2581--2591",
month = dec,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1002/asi.23133",
ISSN = "2330-1643 (print), 2330-1643 (electronic)",
ISSN-L = "2330-1643",
bibdate = "Fri Sep 11 12:15:16 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jasist.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Journal of the Association for Information Science and
Technology",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2330-1643",
onlinedate = "2 May 2014",
}
@Article{Cheang:2014:OMJ,
author = "Brenda Cheang and Samuel Kai Wah Chu and Chongshou Li
and Andrew Lim",
title = "{OR\slash MS} journals evaluation based on a refined
{PageRank} method: an updated and more comprehensive
review",
journal = j-SCIENTOMETRICS,
volume = "100",
number = "2",
pages = "339--361",
month = aug,
year = "2014",
CODEN = "SCNTDX",
DOI = "https://doi.org/10.1007/s11192-014-1272-0",
ISSN = "0138-9130 (print), 1588-2861 (electronic)",
ISSN-L = "0138-9130",
bibdate = "Wed Sep 2 12:06:03 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
URL = "http://link.springer.com/article/10.1007/s11192-014-1272-0",
acknowledgement = ack-nhfb,
fjournal = "Scientometrics",
journal-URL = "http://link.springer.com/journal/11192",
}
@Book{Ding:2014:MSI,
author = "Ying Ding",
title = "Measuring Scholarly Impact: Methods and Practice",
publisher = pub-SV,
address = pub-SV:adr,
pages = "xiv + 346",
year = "2014",
DOI = "https://doi.org/10.1007/978-3-319-10377-8",
ISBN = "3-319-10376-8 (paperback), 3-319-10377-6 (e-book)",
ISBN-13 = "978-3-319-10376-1 (paperback), 978-3-319-10377-8
(e-book)",
LCCN = "Z669.8 .M43 2014",
bibdate = "Wed Feb 22 14:33:58 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib;
z3950.loc.gov:7090/Voyager",
URL = "http://www.loc.gov/catdir/enhancements/fy1501/2014950682-d.html;
http://www.loc.gov/catdir/enhancements/fy1501/2014950682-t.html",
acknowledgement = ack-nhfb,
tableofcontents = "Intro \\
Preface \\
Network Tools and Analysis \\
The Science System \\
Statistical and Text-Based Methods \\
Visualization \\
References \\
Contents \\
Part I: Network Tools and Analysis \\
Chapter 1: Community Detection and Visualization of
Networks with the Map Equation Framework \\
1.1 Introduction \\
1.2 Overview of Methods \\
1.3 The Map Equation Framework \\
1.4 Step-by-Step Instructions to the MapEquation
Software Package \\
References \\
Chapter 2: Link Prediction \\
2.1 Introduction \\
2.2 The Link Prediction Process and Its Applications
\\
2.3 Data \\
2.4 The Linkpred Tool \\
2.5 Link Prediction in Practice \\
Appendix: Usage as a Python Module \\
References \\
Chapter 3: Network Analysis and Indicators \\
3.1 Introduction \\
3.2 Networks and Bibliometrics \\
3.3 Basic Network Properties \\
3.4 Network Data \\
3.5 Scientometrics Through Networks \\
3.6 Collaboration Networks \\
3.7 Citation Networks \\
References \\
Chapter 4: PageRank-Related Methods for Analyzing
Citation Networks \\
4.1 Introduction \\
4.2 PageRank \\
4.3 Literature Review \\
4.4 Tutorial \\
References \\
Part II: The Science System \\
Chapter 5: Systems Life Cycle and Its Relation with the
Triple Helix \\
5.1 Introduction and Motivation \\
5.2 Background Work Related to This Study \\
5.3 Hypothesis to Test \\
5.4 Measurable States During the Life Cycle of a
Technology \\
5.5 Step-by-Step Use of a Tool to Generate Results \\
5.6 Expansion/Evolution of Milestone 5 Concerning
Technology Readiness Levels \\
5.7 Application of TRL Logic to the Modified Model \\
5.8 Discussion \\
References \\
Chapter 6: Spatial Scientometrics and Scholarly Impact:
A Review of Recent Studies, Tools, and Methods \\
6.1 Introduction \\
6.2 Selection of Reviewed Papers \\
6.3 Review \\
References \\
Chapter 7: Researchers' Publication Patterns and Their
Use for Author Disambiguation \\
7.1 Introduction \\
7.2 Previous Studies on the Attribution of Individual
Authors' Publications \\
7.3 Methods \\
7.4 Regularities in Researchers' Publication Patterns
\\
Appendix 1: List of Disciplines Assigned to Journals
\\
Appendix 2: List of Disciplines Assigned to Departments
\\
References \\
Chapter 8: Knowledge Integration and Diffusion:
Measures and Mapping of Diversity and Coherence \\
8.1 Introduction \\
8.2 Conceptual Framework: Knowledge Integration and
Diffusion as Shifts in Cognitive Diversity and
Coherence \\
8.3 Choices on Data and Methods for Operationalisation
\\
8.4 How to Compute and Visualise Knowledge Integration
\\
References \\
Part III: Statistical and Text-Based Methods \\
Chapter 9: Limited Dependent Variable Models and
Probabilistic Prediction in Informetrics \\
9.1 Introduction \\
9.2 The Data: Which Articles Get Cited in Informetrics?
\\
9.3 Binary Regression \\
9.4 Ordinal Regression \\
9.5 Count Data Models \\
9.6 Limited Dependent Variable Models in Stata \\
References \\
Chapter 10: Text Mining with the Stanford CoreNLP \\
10.1 Introduction \\
10.2 Text Mining in Bibliometric Research \\
10.3 Text Mining System Architecture \\
10.4 The Stanford CoreNLP Parser \\
10.5 An Example of Text Mining for Bibliometric
Analysis \\
10.6 Results \\
References \\
Chapter 11: Topic Modeling: Measuring Scholarly Impact
Using a Topical Lens \\
11.1 Introduction \\
11.2 Topic Models \\
11.3 Applying Topic Modeling Methods in Scholarly
Communication \\
11.4 Topic Modeling Tool: Case Study \\
Appendix: Normalization, Mapping, and Clustering
Techniques Used by VOSviewer \\
References \\
Chapter 12: The Substantive and Practical Significance
of Citation Impact Differences Between Institutions:
Guidelines for the \ldots{} \\
12.1 Introduction \\
12.2 Percentile Rankings \\
12.3 Data and Statistical Software \\
12.4 Effect Sizes and related concepts \\
12.5 Cohen's d (for Individual Institutions) \\
12.6 Mean Differences Between Institutions \\
12.7 Proportions (Both for One Institution and for
Comparisons Across Institutions) \\
Appendix: Stata Code Used for These Analyses \\
References \\
Part IV: Visualization \\
Chapter 13: Visualizing Bibliometric Networks \\
13.1 Introduction \\
13.2 Literature Review \\
13.3 Software Tools \\
13.4 Techniques \\
13.5 Tutorials \\
Appendix: Normalization, Mapping, and Clustering
Techniques Used by VOSviewer \\
References \\
Chapter 14: Replicable Science of Science Studies \\
14.1 Open Tools for Science of Science Studies \\
14.2 The Science of Science (Sci2) Tool \\
14.3 Career Trajectories \\
14.4 Discussion and Outlook \\
References \\
Index",
}
@Article{Gleich:2014:MP,
author = "D. F. Gleich and L.-H. Lim and Y. Yu",
title = "Multilinear PageRank",
journal = "arxiv.org",
volume = "arXiv:1409.1465 [cs.NA]",
pages = "1--33",
year = "2014",
bibdate = "Tue Aug 11 16:49:48 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://arxiv.org/pdf/1409.1465v1.pdf",
acknowledgement = ack-nhfb,
}
@Book{Leskovec:2014:MMD,
author = "Jurij Leskovec and Anand Rajaraman and Jeffrey D.
Ullman",
title = "Mining of massive datasets",
publisher = pub-CAMBRIDGE,
address = pub-CAMBRIDGE:adr,
edition = "Second",
pages = "xii + 467",
year = "2014",
DOI = "https://doi.org/10.1017/CBO9781139924801",
ISBN = "1-107-07723-0 (hardcover), 1-316-14731-2 (e-book),
1-139-92480-X (e-book)",
ISBN-13 = "978-1-107-07723-2 (hardcover), 978-1-316-14731-3
(e-book), 978-1-139-92480-1 (e-book)",
LCCN = "QA76.9.D343 R35 2014eb",
bibdate = "Wed Jan 7 11:34:18 MST 2015",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "Written by leading authorities in database and Web
technologies, this book is essential reading for
students and practitioners alike. The popularity of the
Web and Internet commerce provides many extremely large
datasets from which information can be gleaned by data
mining. This book focuses on practical algorithms that
have been used to solve key problems in data mining and
can be applied successfully to even the largest
datasets. It begins with a discussion of the map-reduce
framework, an important tool for parallelizing
algorithms automatically. The authors explain the
tricks of locality-sensitive hashing and stream
processing algorithms for mining data that arrives too
fast for exhaustive processing. Other chapters cover
the PageRank idea and related tricks for organizing the
Web, the problems of finding frequent itemsets and
clustering. This second edition includes new and
extended coverage on social networks, machine learning
and dimensionality reduction.",
acknowledgement = ack-nhfb,
remark = "Previous edition: 2012.",
subject = "Data mining; Big data",
tableofcontents = "Preface \\
1. Data mining \\
2. Map-reduce and the new software stack \\
3. Finding similar items \\
4. Mining data streams \\
5. Link analysis \\
6. Frequent itemsets \\
7. Clustering \\
8. Advertising on the Web \\
9. Recommendation systems \\
10. Mining social-network graphs \\
11. Dimensionality reduction \\
12. Large-scale machine learning \\
Index",
}
@Article{Lofgren:2014:CMC,
author = "Peter Lofgren",
title = "On the complexity of the {Monte Carlo} method for
incremental {PageRank}",
journal = j-INFO-PROC-LETT,
volume = "114",
number = "3",
pages = "104--106",
month = mar,
year = "2014",
CODEN = "IFPLAT",
ISSN = "0020-0190 (print), 1872-6119 (electronic)",
ISSN-L = "0020-0190",
bibdate = "Mon Dec 9 09:33:47 MST 2013",
bibsource = "https://www.math.utah.edu/pub/tex/bib/infoproc2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
http://www.sciencedirect.com/science/journal/00200190",
URL = "http://www.sciencedirect.com/science/article/pii/S0020019013002743",
acknowledgement = ack-nhfb,
fjournal = "Information Processing Letters",
journal-URL = "http://www.sciencedirect.com/science/journal/00200190",
}
@Article{Maehara:2014:CPP,
author = "Takanori Maehara and Takuya Akiba and Yoichi Iwata and
Ken-ichi Kawarabayashi",
title = "Computing personalized {PageRank} quickly by
exploiting graph structures",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "7",
number = "12",
pages = "1023--1034",
month = aug,
year = "2014",
CODEN = "????",
ISSN = "2150-8097",
bibdate = "Wed Feb 4 17:20:26 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
abstract = "We propose a new scalable algorithm that can compute
Personalized PageRank (PPR) very quickly. The Power
method is a state-of-the-art algorithm for computing
exact PPR; however, it requires many iterations. Thus
reducing the number of iterations is the main
challenge. We achieve this by exploiting graph
structures of web graphs and social networks. The
convergence of our algorithm is very fast. In fact, it
requires up to 7.5 times fewer iterations than the
Power method and is up to five times faster in actual
computation time. To the best of our knowledge, this is
the first time to use graph structures explicitly to
solve PPR quickly. Our contributions can be summarized
as follows. 1. We provide an algorithm for computing a
tree decomposition, which is more efficient and
scalable than any previous algorithm. 2. Using the
above algorithm, we can obtain a core-tree
decomposition of any web graph and social network. This
allows us to decompose a web graph and a social network
into (1) the core, which behaves like an expander
graph, and (2) a small tree-width graph, which behaves
like a tree in an algorithmic sense. 3. We apply a
direct method to the small tree-width graph to
construct an LU decomposition. 4. Building on the LU
decomposition and using it as pre-conditioner, we apply
GMRES method (a state-of-the-art advanced iterative
method) to compute PPR for whole web graphs and social
networks.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1174",
}
@Article{Nykl:2014:PVE,
author = "Michal Nykl and Karel Jezek and Dalibor Fiala and
Martin Dostal",
title = "{PageRank} variants in the evaluation of citation
networks",
journal = j-J-INFORMETRICS,
volume = "8",
number = "3",
pages = "683--692",
month = jul,
year = "2014",
CODEN = "????",
ISSN = "1751-1577 (print), 1875-5879 (electronic)",
ISSN-L = "1751-1577",
bibdate = "Wed Sep 9 16:29:51 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S1751157714000583",
acknowledgement = ack-nhfb,
fjournal = "Journal of Informetrics",
journal-URL = "http://www.sciencedirect.com/science/journal/17511577/",
}
@Article{Salkuyeh:2014:PPG,
author = "Davod Khojasteh Salkuyeh and Vahid Edalatpour and
Davod Hezari",
title = "Polynomial Preconditioning for the {GeneRank}
Problem",
journal = j-ELECTRON-TRANS-NUMER-ANAL,
volume = "41",
pages = "179--189",
year = "2014",
CODEN = "????",
ISSN = "1068-9613 (print), 1097-4067 (electronic)",
ISSN-L = "1068-9613",
MRclass = "92D10 (65F10 65F50)",
MRnumber = "3232104",
bibdate = "Mon Apr 3 06:27:15 MDT 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/etna.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://etna.mcs.kent.edu/vol.41.2014/pp179-189.dir/pp179-189.pdf;
http://etna.mcs.kent.edu/volumes/2011-2020/vol41/abstract.php?vol=41&pages=179-189",
acknowledgement = ack-nhfb,
fjournal = "Electronic Transactions on Numerical Analysis",
journal-URL = "http://etna.mcs.kent.edu/",
}
@Article{Wang:2014:GRC,
author = "Qing Wang and Siyi Zhang and Shichao Pang and Menghuan
Zhang and Bo Wang and Qi Liu and Jing Li",
title = "{GroupRank}: Rank Candidate Genes in {PPI} Network by
Differentially Expressed Gene Groups",
journal = j-PLOS-ONE,
volume = "9",
number = "10",
pages = "e110406:1--e110406:7",
month = oct,
year = "2014",
CODEN = "POLNCL",
DOI = "https://doi.org/10.1371/journal.pone.0110406",
ISSN = "1932-6203",
bibdate = "Wed Aug 12 08:52:01 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110406",
abstract = "Many cell activities are organized as a network, and
genes are clustered into co-expressed groups if they
have the same or closely related biological function or
they are co-regulated. In this study, based on an
assumption that a strong candidate disease gene is more
likely close to gene groups in which all members
coordinately differentially express than individual
genes with differential expression, we developed a
novel disease gene prioritization method GroupRank by
integrating gene co-expression and differential
expression information generated from microarray data
as well as PPI network. A candidate gene is ranked high
using GroupRank if it is differentially expressed in
disease and control or is close to differentially
co-expressed groups in PPI network. We tested our
method on data sets of lung, kidney, leukemia and
breast cancer. The results revealed GroupRank could
efficiently prioritize disease genes with significantly
improved AUC value in comparison to the previous method
with no consideration of co-expressed gene groups in
PPI network. Moreover, the functional analyses of the
major contributing gene group in gene prioritization of
kidney cancer verified that our algorithm GroupRank not
only ranks disease genes efficiently but also could
help us identify and understand possible mechanisms in
important physiological and pathological processes of
disease.",
acknowledgement = ack-nhfb,
fjournal = "PLoS One",
journal-URL = "http://www.plosone.org/",
}
@Article{Yan:2014:TBP,
author = "Erjia Yan",
title = "Topic-based {PageRank}: toward a topic-level
scientific evaluation",
journal = j-SCIENTOMETRICS,
volume = "100",
number = "2",
pages = "407--437",
month = aug,
year = "2014",
CODEN = "SCNTDX",
DOI = "https://doi.org/10.1007/s11192-014-1308-5",
ISSN = "0138-9130 (print), 1588-2861 (electronic)",
ISSN-L = "0138-9130",
bibdate = "Wed Sep 2 12:06:03 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
URL = "http://link.springer.com/article/10.1007/s11192-014-1308-5",
acknowledgement = ack-nhfb,
fjournal = "Scientometrics",
journal-URL = "http://link.springer.com/journal/11192",
}
@Article{Chen:2015:AAS,
author = "Hung-Hsuan Chen and C. Lee Giles",
title = "{ASCOS++}: an Asymmetric Similarity Measure for
Weighted Networks to Address the Problem of {SimRank}",
journal = j-TKDD,
volume = "10",
number = "2",
pages = "15:1--15:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2776894",
ISSN = "1556-4681 (print), 1556-472X (electronic)",
ISSN-L = "1556-4681",
bibdate = "Mon Oct 26 17:19:18 MDT 2015",
bibsource = "http://www.acm.org/pubs/contents/journals/tkdd/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tkdd.bib",
abstract = "In this article, we explore the relationships among
digital objects in terms of their similarity based on
vertex similarity measures. We argue that SimRank --- a
famous similarity measure --- and its families, such as
P-Rank and SimRank++, fail to capture similar node
pairs in certain conditions, especially when two nodes
can only reach each other through paths of odd lengths.
We present new similarity measures ASCOS and ASCOS++ to
address the problem. ASCOS outputs a more complete
similarity score than SimRank and SimRank's families.
ASCOS++ enriches ASCOS to include edge weight into the
measure, giving all edges and network weights an
opportunity to make their contribution. We show that
both ASCOS++ and ASCOS can be reformulated and applied
on a distributed environment for parallel contribution.
Experimental results show that ASCOS++ reports a better
score than SimRank and several famous similarity
measures. Finally, we re-examine previous use cases of
SimRank, and explain appropriate and inappropriate use
cases. We suggest future SimRank users following the
rules proposed here before na{\"\i}vely applying it. We
also discuss the relationship between ASCOS++ and
PageRank.",
acknowledgement = ack-nhfb,
articleno = "15",
fjournal = "ACM Transactions on Knowledge Discovery from Data
(TKDD)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1054",
}
@Article{Dong:2015:APP,
author = "Wenqiang Dong and Fulai Wang and Yu Huang and
Guangluan Xu and Zhi Guo and Xingyu Fu and Kun Fu",
title = "An advanced pre-positioning method for the
force-directed graph visualization based on {PageRank}
algorithm",
journal = j-COMPUTERS-AND-GRAPHICS,
volume = "47",
number = "??",
pages = "24--33",
month = apr,
year = "2015",
CODEN = "COGRD2",
ISSN = "0097-8493 (print), 1873-7684 (electronic)",
ISSN-L = "0097-8493",
bibdate = "Sat Mar 14 08:21:38 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compgraph.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0097849314001277",
acknowledgement = ack-nhfb,
fjournal = "Computers \& Graphics",
journal-URL = "http://www.sciencedirect.com/science/journal/00978493/",
}
@Article{Fiala:2015:DPB,
author = "Dalibor Fiala and Lovro Subelj and Slavko Zitnik and
Marko Bajec",
title = "Do {PageRank}-based author rankings outperform simple
citation counts?",
journal = j-J-INFORMETRICS,
volume = "9",
number = "2",
pages = "334--348",
month = apr,
year = "2015",
CODEN = "????",
ISSN = "1751-1577 (print), 1875-5879 (electronic)",
ISSN-L = "1751-1577",
bibdate = "Wed Sep 9 16:29:52 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S1751157715000267",
acknowledgement = ack-nhfb,
fjournal = "Journal of Informetrics",
journal-URL = "http://www.sciencedirect.com/science/journal/17511577/",
}
@Article{Gleich:2015:MP,
author = "David F. Gleich and Lek-Heng Lim and Yongyang Yu",
title = "Multilinear {PageRank}",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "36",
number = "4",
pages = "1507--1541",
month = "????",
year = "2015",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/140985160",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
bibdate = "Tue Feb 9 08:35:01 MST 2016",
bibsource = "http://epubs.siam.org/sam-bin/dbq/toc/SIMAX/36/4;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/siamjmatanaappl.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
onlinedate = "January 2015",
}
@Article{Gleich:2015:PBW,
author = "David F. Gleich",
title = "{PageRank} Beyond the {Web}",
journal = j-SIAM-REVIEW,
volume = "57",
number = "3",
pages = "321--363",
month = "????",
year = "2015",
CODEN = "SIREAD",
DOI = "https://doi.org/10.1137/140976649",
ISSN = "0036-1445 (print), 1095-7200 (electronic)",
ISSN-L = "0036-1445",
bibdate = "Sat Aug 8 06:17:25 MDT 2015",
bibsource = "http://epubs.siam.org/toc/siread/57/3;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/siamreview.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Review",
journal-URL = "http://epubs.siam.org/sirev",
keywords = "AuthorRank; BadRank; BookRank; BuddyRank; CiteRank;
DirRank; FactRank; FolkRank; GeneRank; HostRank;
IsoRank; ItemRank; MonitorRank; ObjectRank; PageRank;
PopRank; ProteinRank; TimedPageRank; TrustRank;
TwitterRank; VisualRank",
onlinedate = "January 2015",
}
@Article{Grolmusz:2015:NPU,
author = "Vince Grolmusz",
title = "A note on the {PageRank} of undirected graphs",
journal = j-INFO-PROC-LETT,
volume = "115",
number = "6--8",
pages = "633--634",
month = jun # "\slash " # aug,
year = "2015",
CODEN = "IFPLAT",
ISSN = "0020-0190 (print), 1872-6119 (electronic)",
ISSN-L = "0020-0190",
bibdate = "Thu May 28 06:03:49 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/infoproc2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0020019015000381",
acknowledgement = ack-nhfb,
fjournal = "Information Processing Letters",
journal-URL = "http://www.sciencedirect.com/science/journal/00200190/",
}
@Article{Gu:2015:TSM,
author = "Chuanqing Gu and Fei Xie and Ke Zhang",
title = "A two-step matrix splitting iteration for computing
{PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "278",
number = "??",
pages = "19--28",
day = "15",
month = apr,
year = "2015",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:34:48 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042714004294",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Huang:2015:PMI,
author = "Na Huang and Chang-Feng Ma",
title = "Parallel multisplitting iteration methods based on
{$M$}-splitting for the {PageRank} problem",
journal = j-APPL-MATH-COMP,
volume = "271",
number = "??",
pages = "337--343",
day = "15",
month = nov,
year = "2015",
CODEN = "AMHCBQ",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Fri Nov 13 08:52:33 MST 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300315012345",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003/",
}
@Article{Li:2015:WCP,
author = "Zhenguo Li and Yixiang Fang and Qin Liu and Jiefeng
Cheng and Reynold Cheng and John C. S. Lui",
title = "Walking in the cloud: parallel {SimRank} at scale",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "9",
number = "1",
pages = "24--35",
month = sep,
year = "2015",
CODEN = "????",
ISSN = "2150-8097",
bibdate = "Sat Dec 19 17:42:24 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
abstract = "Despite its popularity, SimRank is computationally
costly, in both time and space. In particular, its
recursive nature poses a great challenge in using
modern distributed computing power, and also prevents
querying similarities individually. Existing solutions
suffer greatly from these practical issues. In this
paper, we break such dependency for maximum efficiency
possible. Our method consists of offline and online
phases. In offline phase, a length- n indexing vector
is derived by solving a linear system in parallel. At
online query time, the similarities are computed
instantly from the index vector. Throughout, the Monte
Carlo method is used to maximally reduce time and
space. Our algorithm, called CloudWalker, is highly
parallelizable, with only linear time and space.
Remarkably, it responses to both single-pair and
single-source queries in constant time. CloudWalker is
orders of magnitude more efficient and scalable than
existing solutions for large-scale problems.
Implemented on Spark with 10 machines and tested on the
web-scale clue-web graph with 1 billion nodes and 43
billion edges, it takes 110 hours for offline indexing,
64 seconds for a single-pair query, and 188 seconds for
a single-source query. To the best of our knowledge,
our work is the first to report results on clue-web,
which is 10x larger than the largest graph ever
reported for SimRank computation.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1174",
}
@Article{Liu:2015:PCU,
author = "Zifan Liu and Nahid Emad and Soufian Ben Amor",
title = "{PageRank} Computation Using a Multiple Implicitly
Restarted {Arnoldi} Method for Modeling Epidemic
Spread",
journal = j-INT-J-PARALLEL-PROG,
volume = "43",
number = "6",
pages = "1028--1053",
month = dec,
year = "2015",
CODEN = "IJPPE5",
DOI = "https://doi.org/10.1007/s10766-014-0344-3",
ISSN = "0885-7458 (print), 1573-7640 (electronic)",
ISSN-L = "0885-7458",
bibdate = "Tue Sep 29 10:13:48 MDT 2015",
bibsource = "http://link.springer.com/journal/10766/43/6;
https://www.math.utah.edu/pub/tex/bib/intjparallelprogram.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/article/10.1007/s10766-014-0344-3",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Parallel Programming",
journal-URL = "http://link.springer.com/journal/10766",
}
@Article{Mitliagkas:2015:FFP,
author = "Ioannis Mitliagkas and Michael Borokhovich and
Alexandros G. Dimakis and Constantine Caramanis",
title = "{FrogWild!}: fast {PageRank} approximations on graph
engines",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "8",
number = "8",
pages = "874--885",
month = apr,
year = "2015",
CODEN = "????",
ISSN = "2150-8097",
bibdate = "Wed Apr 15 19:02:29 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
abstract = "We propose FrogWild, a novel algorithm for fast
approximation of high PageRank vertices, geared towards
reducing network costs of running traditional PageRank
algorithms. Our algorithm can be seen as a quantized
version of power iteration that performs multiple
parallel random walks over a directed graph. One
important innovation is that we introduce a
modification to the GraphLab framework that only
partially synchronizes mirror vertices. This partial
synchronization vastly reduces the network traffic
generated by traditional PageRank algorithms, thus
greatly reducing the per-iteration cost of PageRank. On
the other hand, this partial synchronization also
creates dependencies between the random walks used to
estimate PageRank. Our main theoretical innovation is
the analysis of the correlations introduced by this
partial synchronization process and a bound
establishing that our approximation is close to the
true PageRank vector. We implement our algorithm in
GraphLab and compare it against the default PageRank
implementation. We show that our algorithm is very
fast, performing each iteration in less than one second
on the Twitter graph and can be up to $ 7 \times $
faster compared to the standard GraphLab PageRank
implementation.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1174",
}
@Article{Nykl:2015:ARB,
author = "Michal Nykl and Michal Campr and Karel Jezek",
title = "Author ranking based on personalized {PageRank}",
journal = j-J-INFORMETRICS,
volume = "9",
number = "4",
pages = "777--799",
month = oct,
year = "2015",
CODEN = "????",
ISSN = "1751-1577 (print), 1875-5879 (electronic)",
ISSN-L = "1751-1577",
bibdate = "Wed Sep 9 16:29:53 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S1751157715200181",
acknowledgement = ack-nhfb,
fjournal = "Journal of Informetrics",
journal-URL = "http://www.sciencedirect.com/science/journal/17511577/",
}
@Article{Peng:2015:IPC,
author = "Wei Peng and Jianxin Wang and Bihai Zhao and Lusheng
Wang",
title = "Identification of protein complexes using weighted
{PageRank--Nibble} algorithm and core-attachment
structure",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "179--192",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343954",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein complexes play a significant role in
understanding the underlying mechanism of most cellular
functions. Recently, many researchers have explored
computational methods to identify protein complexes
from protein-protein interaction (PPI) networks. One
group of researchers focus on detecting local dense
subgraphs which correspond to protein complexes by
considering local neighbors. The drawback of this kind
of approach is that the global information of the
networks is ignored. Some methods such as Markov
Clustering algorithm (MCL), PageRank--Nibble are
proposed to find protein complexes based on random walk
technique which can exploit the global structure of
networks. However, these methods ignore the inherent
core-attachment structure of protein complexes and
treat adjacent node equally. In this paper, we design a
weighted PageRank--Nibble algorithm which assigns each
adjacent node with different probability, and propose a
novel method named WPNCA to detect protein complex from
PPI networks by using weighted PageRank--Nibble
algorithm and core-attachment structure. Firstly, WPNCA
partitions the PPI networks into multiple dense
clusters by using weighted PageRank--Nibble algorithm.
Then the cores of these clusters are detected and the
rest of proteins in the clusters will be selected as
attachments to form the final predicted protein
complexes. The experiments on yeast data show that
WPNCA outperforms the existing methods in terms of both
accuracy and p-value. The software for WPNCA is
available at
``http://netlab.csu.edu.cn/bioinfomatics/weipeng/WPNCA/download.html''",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pop:2015:AMS,
author = "Florin Pop and Radu-Ioan Ciobanu and Ciprian Dobre",
title = "Adaptive method to support social-based mobile
networks using a {PageRank} approach",
journal = j-CCPE,
volume = "27",
number = "8",
pages = "1900--1912",
day = "10",
month = jun,
year = "2015",
CODEN = "CCPEBO",
DOI = "https://doi.org/10.1002/cpe.3103",
ISSN = "1532-0626 (print), 1532-0634 (electronic)",
ISSN-L = "1532-0626",
bibdate = "Sat Jul 25 19:54:07 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ccpe.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Concurrency and Computation: Practice and Experience",
journal-URL = "http://www.interscience.wiley.com/jpages/1532-0626",
onlinedate = "23 Jul 2013",
}
@Article{Sarma:2015:FDP,
author = "Atish Das Sarma and Anisur Rahaman Molla and Gopal
Pandurangan and Eli Upfal",
title = "Fast distributed {PageRank} computation",
journal = j-THEOR-COMP-SCI,
volume = "561 (part B)",
number = "??",
pages = "113--121",
day = "4",
month = jan,
year = "2015",
CODEN = "TCSCDI",
ISSN = "0304-3975 (print), 1879-2294 (electronic)",
ISSN-L = "0304-3975",
bibdate = "Tue Dec 2 19:05:34 MST 2014",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tcs2010.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0304397514002709",
acknowledgement = ack-nhfb,
fjournal = "Theoretical Computer Science",
journal-URL = "http://www.sciencedirect.com/science/journal/03043975/",
}
@Article{Zhu:2015:SAP,
author = "Fanwei Zhu and Yuan Fang and Kevin Chen-Chuan Chang
and Jing Ying",
title = "Scheduled approximation for {Personalized PageRank}
with {Utility-based Hub Selection}",
journal = j-VLDB-J,
volume = "24",
number = "5",
pages = "655--679",
month = oct,
year = "2015",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-014-0376-8",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Fri Sep 18 06:51:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
abstract = "As Personalized PageRank has been widely leveraged for
ranking on a graph, the efficient computation of
Personalized PageRank Vector (PPV) becomes a prominent
issue. In this paper, we propose FastPPV, an
approximate PPV computation algorithm that is
incremental and accuracy-aware. Our approach hinges on
a novel paradigm of scheduled approximation: the
computation is partitioned and scheduled for processing
in an ``organized'' way, such that we can gradually
improve our PPV estimation in an incremental manner and
quantify the accuracy of our approximation at query
time. Guided by this principle, we develop an efficient
hub-based realization, where we adopt the metric of hub
length to partition and schedule random walk tours so
that the approximation error reduces exponentially over
iterations. In addition, as tours are segmented by
hubs, the shared substructures between different tours
(around the same hub) can be reused to speed up query
processing both within and across iterations. Given the
key roles played by the hubs, we further investigate
the problem of hub selection. In particular, we develop
a conceptual model to select hubs based on the two
desirable properties of hubs--sharing and
discriminating, and present several different
strategies to realize the conceptual model. Finally, we
evaluate FastPPV over two real-world graphs, and show
that it not only significantly outperforms two
state-of-the-art baselines in both online and offline
phrases, but also scales well on larger graphs. In
particular, we are able to achieve near-constant time
online query processing irrespective of graph size.",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Agryzkov:2016:NHN,
author = "Taras Agryzkov and Leandro Tortosa and Jose F.
Vicent",
title = "New highlights and a new centrality measure based on
the {Adapted PageRank Algorithm} for urban networks",
journal = j-APPL-MATH-COMP,
volume = "291",
number = "??",
pages = "14--29",
day = "1",
month = dec,
year = "2016",
CODEN = "AMHCBQ",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Wed Sep 28 06:57:06 MDT 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300316304076",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003/",
}
@Book{Arbenz:2016:LNS,
author = "Peter Arbenz",
title = "Lecture Notes on Solving Large Scale Eigenvalue
Problems",
publisher = "Computer Science Department, ETH Z{\"u}rich",
address = "Z{\"u}rich, Switzerland",
pages = "vi + 259",
year = "2016",
bibdate = "Mon Sep 04 10:05:42 2023",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/h/hartree-douglas-r.bib;
https://www.math.utah.edu/pub/bibnet/authors/l/lanczos-cornelius.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://people.inf.ethz.ch/arbenz/ewp/Lnotes/lsevp.pdf",
acknowledgement = ack-nhfb,
tableofcontents = "1 Introduction / 1 \\
1.1 What makes eigenvalues interesting? / 1 \\
1.2 Example 1: The vibrating string / 2 \\
1.2.1 Problem setting / 2 \\
1.2.2 The method of separation of variables / 5 \\
1.3 Numerical methods for solving 1-dimensional
problems / 6 \\
1.3.1 Finite differences / 6 \\
1.3.2 The finite element method / 7 \\
1.3.3 Global functions / 8 \\
1.3.4 A numerical comparison / 9 \\
1.4 Example 2: The heat equation / 9 \\
1.5 Example 3: The wave equation / 12 \\
1.6 The 2D Laplace eigenvalue problem / 13 \\
1.6.1 The finite difference method / 13 \\
1.6.2 The finite element method (FEM) / 16 \\
1.6.3 A numerical example / 20 \\
1.7 Cavity resonances in particle accelerators / 21 \\
1.8 Spectral clustering / 23 \\
1.8.1 The graph Laplacian / 24 \\
1.8.2 Spectral clustering / 25 \\
1.8.3 Normalized graph Laplacians / 27 \\
1.9 Google's PageRank / 28 \\
1.10 Other sources of eigenvalue problems / 30 \\
Bibliography / 31 \\
2 Basics / 33 \\
2.1 Notation / 33 \\
2.2 Statement of the problem / 34 \\
2.3 Similarity transformations / 37 \\
2.4 Schur decomposition / 38 \\
2.5 The real Schur decomposition / 39 \\
2.6 Normal matrices / 40 \\
2.7 Hermitian matrices / 41 \\
2.8 The Jordan normal form / 43 \\
2.9 Projections / 45 \\
2.10 The Rayleigh quotient / 47 \\
2.11 Cholesky factorization / 49 \\
2.12 The singular value decomposition (SVD) / 50 \\
Bibliography / 52 \\
3 Newton methods / 53 \\
3.1 Linear and nonlinear eigenvalue problems / 53 \\
3.2 Zeros of the determinant / 54 \\
3.2.1 Algorithmic differentiation / 55 \\
3.2.2 Hyman's algorithm / 55 \\
3.2.3 Computing multiple zeros / 58 \\
3.3 Newton methods for the constrained matrix problem /
58 \\
3.4 Successive linear approximations / 60 \\
Bibliography / 61 \\
4 The $ Q R $ Algorithm / 63 \\
4.1 The basic $ Q R $ algorithm / 63 \\
4.1.1 Numerical experiments / 64 \\
4.2 The Hessenberg $ Q R $ algorithm / 67 \\
4.2.1 A numerical experiment / 69 \\
4.2.2 Complexity / 70 \\
4.3 The Householder reduction to Hessenberg form / 71
\\
4.3.1 Householder reflectors / 71 \\
4.3.2 Reduction to Hessenberg form / 71 \\
4.4 Improving the convergence of the $ Q R $ algorithm
/ 73 \\
4.4.1 A numerical example / 74 \\
4.4.2 $ Q R $ algorithm with shifts / 75 \\
4.4.3 A numerical example / 76 \\
4.5 The double shift $ Q R $ algorithm / 77 \\
4.5.1 A numerical example / 81 \\
4.5.2 The complexity / 83 \\
4.6 The symmetric tridiagonal $ Q R $ algorithm / 84
\\
4.6.1 Reduction to tridiagonal form / 84 \\
4.6.2 The tridiagonal $ Q R $ algorithm / 85 \\
4.7 Research / 87 \\
4.8 Summary / 87 \\
Bibliography / 88 \\
5 Cuppen's Divide and Conquer Algorithm / 91 \\
5.1 The divide and conquer idea / 91 \\
5.2 Partitioning the tridiagonal matrix / 92 \\
5.3 Solving the small systems / 92 \\
5.4 Deflation / 93 \\
5.4.1 Numerical examples / 94 \\
5.5 The eigenvalue problem for $D + \rho v v^T$ / 95
\\
5.6 Solving the secular equation / 98 \\
5.7 A first algorithm / 99 \\
5.7.1 A numerical example / 100 \\
5.8 The algorithm of Gu and Eisenstat / 103 \\
5.8.1 A numerical example [continued] / 104 \\
Bibliography / 107 \\
6 LAPACK and the BLAS / 109 \\
6.1 LAPACK / 109 \\
6.2 BLAS / 110 \\
6.2.1 Typical performance numbers for the BLAS / 111
\\
6.3 Blocking / 113 \\
6.4 LAPACK solvers for the symmetric eigenproblems /
114 \\
6.5 Generalized Symmetric Definite Eigenproblems (GSEP)
/ 116 \\
6.6 An example of a LAPACK routines / 116 \\
Bibliography / 122 \\
7 Vector iteration (power method) / 125 \\
7.1 Simple vector iteration / 125 \\
7.2 Angles between vectors / 126 \\
7.3 Convergence analysis / 127 \\
7.4 A numerical example / 130 \\
7.5 The symmetric case / 131 \\
7.6 Inverse vector iteration / 135 \\
7.7 The generalized eigenvalue problem / 139 \\
7.8 Computing higher eigenvalues / 139 \\
7.9 Rayleigh quotient iteration / 140 \\
7.9.1 A numerical example / 143 \\
Bibliography / 144 \\
8 Simultaneous vector or subspace iterations / 145 \\
8.1 Basic subspace iteration / 145 \\
8.2 Angles between subspaces / 146 \\
8.3 Convergence of basic subspace iteration / 148 \\
8.4 Accelerating subspace iteration / 153 \\
8.5 Relation between subspace iteration and $ Q R $
algorithm / 158 \\
8.6 Addendum / 161 \\
Bibliography / 161 \\
9 Krylov subspaces / 163 \\
9.1 Introduction / 163 \\
9.2 Definition and basic properties / 164 \\
9.3 Polynomial representation of Krylov subspaces / 165
\\
9.4 Error bounds of Saad / 168 \\
Bibliography / 171 \\
10 Arnoldi and Lanczos algorithms / 173 \\
10.1 An orthonormal basis for the Krylov space Kj (x) /
173 \\
10.2 Arnoldi algorithm with explicit restarts / 175 \\
10.3 The Lanczos basis / 176 \\
10.4 The Lanczos process as an iterative method / 178
\\
10.5 An error analysis of the unmodified Lanczos
algorithm / 185 \\
10.6 Partial reorthogonalization / 187 \\
10.7 Block Lanczos / 190 \\
10.8 External selective reorthogonalization / 193 \\
Bibliography / 194 \\
11 Restarting Arnoldi and Lanczos algorithms / 195 \\
11.1 The $m$-step Arnoldi iteration / 195 \\
11.2 Implicit restart / 196 \\
11.3 Convergence criterion / 198 \\
11.4 The generalized eigenvalue problem / 199 \\
11.5 A numerical example / 201 \\
11.6 Another numerical example / 206 \\
11.7 The Lanczos algorithm with thick restarts / 210
\\
11.8 Krylov--Schur algorithm / 213 \\
11.9 The rational Krylov space method / 214 \\
Bibliography / 215 \\
12 The Jacobi--Davidson Method / 217 \\
12.1 The Davidson algorithm / 217 \\
12.2 The Jacobi orthogonal component correction / 218
\\
12.2.1 Restarts / 221 \\
12.2.2 The computation of several eigenvalues / 221 \\
12.2.3 Spectral shifts / 222 \\
12.3 The generalized Hermitian eigenvalue problem / 224
\\
12.4 A numerical example / 224 \\
12.5 The Jacobi--Davidson algorithm for interior
eigenvalues / 228 \\
12.6 Harmonic Ritz values and vectors / 229 \\
12.7 Refined Ritz vectors / 231 \\
12.8 The generalized Schur decomposition / 233 \\
12.9 JDQZ: Computing a partial $ Q Z $ decomposition /
233 \\
12.9.1 Restart / 235 \\
12.9.2 Deflation / 235 \\
12.9.3 Algorithm / 236 \\
12.10 Jacobi--Davidson for nonlinear eigenvalue
problems / 236 \\
Bibliography / 239 \\
13 Rayleigh quotient and trace minimization / 241 \\
13.1 Introduction / 241 \\
13.2 The method of steepest descent / 242 \\
13.3 The conjugate gradient algorithm / 243 \\
13.4 Locally optimal PCG (LOPCG) / 247 \\
13.5 The block Rayleigh quotient minimization algorithm
(BRQMIN) / 250 \\
13.6 The locally-optimal block preconditioned conjugate
gradient method (LOBPCG) / 250 \\
13.7 A numerical example / 251 \\
13.8 Trace minimization / 253 \\
Bibliography / 258",
}
@Article{Mehrabian:2016:SWR,
author = "Abbas Mehrabian and Nick Wormald",
title = "It's a Small World for Random Surfers",
journal = j-ALGORITHMICA,
volume = "76",
number = "2",
pages = "344--380",
month = oct,
year = "2016",
CODEN = "ALGOEJ",
DOI = "https://doi.org/10.1007/s00453-015-0034-6",
ISSN = "0178-4617 (print), 1432-0541 (electronic)",
ISSN-L = "0178-4617",
bibdate = "Tue Sep 20 10:36:26 MDT 2016",
bibsource = "http://link.springer.com/journal/453/76/2;
https://www.math.utah.edu/pub/tex/bib/algorithmica.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://link.springer.com/article/10.1007/s00453-015-0034-6",
acknowledgement = ack-nhfb,
fjournal = "Algorithmica",
journal-URL = "http://link.springer.com/journal/453",
keywords = "Height of random trees; Large deviations;
PageRank-based selection model; Probabilistic analysis;
Random-surfer; Small-world phenomenon; Webgraph model",
}
@Article{Wang:2016:HEI,
author = "Sibo Wang and Youze Tang and Xiaokui Xiao and Yin Yang
and Zengxiang Li",
title = "{HubPPR}: effective indexing for approximate
personalized pagerank",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "10",
number = "3",
pages = "205--216",
month = nov,
year = "2016",
CODEN = "????",
ISSN = "2150-8097",
bibdate = "Thu Dec 1 09:02:03 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
abstract = "Personalized PageRank (PPR) computation is a
fundamental operation in web search, social networks,
and graph analysis. Given a graph $G$, a source $s$,
and a target $t$, the PPR query $ \Pi (s, t)$ returns
the probability that a random walk on $G$ starting from
$s$ terminates at $t$. Unlike global PageRank which can
be effectively pre-computed and materialized, the PPR
result depends on both the source and the target,
rendering results materialization infeasible for large
graphs. Existing indexing techniques have rather
limited effectiveness; in fact, the current
state-of-the-art solution, BiPPR, answers individual
PPR queries without pre-computation or indexing, and
yet it outperforms all previous index-based solutions.
Motivated by this, we propose HubPPR, an effective
indexing scheme for PPR computation with controllable
tradeoffs for accuracy, query time, and memory
consumption. The main idea is to pre-compute and index
auxiliary information for selected hub nodes that are
often involved in PPR processing. Going one step
further, we extend HubPPR to answer top-$k$ PPR
queries, which returns the $k$ nodes with the highest
PPR values with respect to a source $s$, among a given
set $T$ of target nodes. Extensive experiments
demonstrate that compared to the current best solution
BiPPR, HubPPR achieves up to 10x and 220x speedup for
PPR and top-$k$ PPR processing, respectively, with
moderate memory consumption. Notably, with a single
commodity server, HubPPR answers a top-$k$ PPR query in
seconds on graphs with billions of edges, with high
accuracy and strong result quality guarantees.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1174",
}
@Article{Zhang:2016:FAE,
author = "Hong-Fan Zhang and Ting-Zhu Huang and Chun Wen and
Zhao-Li Shen",
title = "{FOM} accelerated by an extrapolation method for
solving {PageRank} problems",
journal = j-J-COMPUT-APPL-MATH,
volume = "296",
number = "??",
pages = "397--409",
month = apr,
year = "2016",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:34:55 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042715004793",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Fiala:2017:PBP,
author = "Dalibor Fiala and Gabriel Tutoky",
title = "{PageRank}-based prediction of award-winning
researchers and the impact of citations",
journal = j-J-INFORMETRICS,
volume = "11",
number = "4",
pages = "1044--1068",
month = nov,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.joi.2017.09.008",
ISSN = "1751-1577 (print), 1875-5879 (electronic)",
ISSN-L = "1751-1577",
bibdate = "Thu Jul 26 06:36:09 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S175115771730038X",
acknowledgement = ack-nhfb,
fjournal = "Journal of Informetrics",
journal-URL = "http://www.sciencedirect.com/science/journal/17511577/",
}
@Article{Gu:2017:AIA,
author = "Chuanqing Gu and Wenwen Wang",
title = "An {Arnoldi--Inout} algorithm for computing {PageRank}
problems",
journal = j-J-COMPUT-APPL-MATH,
volume = "309",
number = "??",
pages = "219--229",
day = "1",
month = jan,
year = "2017",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:35:53 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042716302606",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Guo:2017:PPP,
author = "Wentian Guo and Yuchen Li and Mo Sha and Kian-Lee
Tan",
title = "Parallel personalized pagerank on dynamic graphs",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "11",
number = "1",
pages = "93--106",
month = sep,
year = "2017",
CODEN = "????",
ISSN = "2150-8097",
bibdate = "Tue Oct 10 17:16:21 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
abstract = "Personalized PageRank (PPR) is a well-known proximity
measure in graphs. To meet the need for dynamic PPR
maintenance, recent works have proposed a local update
scheme to support incremental computation.
Nevertheless, sequential execution of the scheme is
still too slow for highspeed stream processing.
Therefore, we are motivated to design a parallel
approach for dynamic PPR computation. First, as updates
always come in batches, we devise a batch processing
method to reduce synchronization cost among every
single update and enable more parallelism for iterative
parallel execution. Our theoretical analysis shows that
the parallel approach has the same asymptotic
complexity as the sequential approach. Second, we
devise novel optimization techniques to effectively
reduce runtime overheads for parallel processes.
Experimental evaluation shows that our parallel
algorithm can achieve orders of magnitude speedups on
GPUs and multi-core CPUs compared with the
state-of-the-art sequential algorithm.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1174",
}
@Article{Lai:2017:PCL,
author = "Siyan Lai and Bo Shao and Ying Xu and Xiaola Lin",
title = "Parallel computations of local {PageRank} problem
based on {Graphics Processing Unit}",
journal = j-CCPE,
volume = "29",
number = "24",
pages = "??--??",
day = "25",
month = dec,
year = "2017",
CODEN = "CCPEBO",
DOI = "https://doi.org/10.1002/cpe.4245",
ISSN = "1532-0626 (print), 1532-0634 (electronic)",
ISSN-L = "1532-0626",
bibdate = "Sat Dec 30 09:11:59 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ccpe.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Concurrency and Computation: Practice and Experience",
journal-URL = "http://www.interscience.wiley.com/jpages/1532-0626",
}
@Article{Lai:2017:SIP,
author = "Siyan Lai and Bo Shao and Ying Xu and Xiaola Lin",
title = "Parallel computations of local {PageRank} problem
based on {Graphics Processing Unit}",
journal = j-CCPE,
volume = "29",
number = "24",
pages = "??--??",
day = "25",
month = dec,
year = "2017",
CODEN = "CCPEBO",
DOI = "https://doi.org/10.1002/cpe.4245",
ISSN = "1532-0626 (print), 1532-0634 (electronic)",
ISSN-L = "1532-0626",
bibdate = "Sat Dec 30 09:11:59 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ccpe.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Concurrency and Computation: Practice and Experience",
journal-URL = "http://www.interscience.wiley.com/jpages/1532-0626",
}
@Article{Li:2017:UMP,
author = "Wen Li and Dongdong Liu and Michael K. Ng and
Seak-Weng Vong",
title = "The uniqueness of multilinear {PageRank} vectors",
journal = j-NUM-LIN-ALG-APPL,
volume = "24",
number = "6",
pages = "??--??",
month = dec,
year = "2017",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.2107",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
bibdate = "Sat Dec 30 08:27:16 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506",
}
@Article{Liu:2017:IPV,
author = "Qi Liu and Biao Xiang and Nicholas Jing Yuan and
Enhong Chen and Hui Xiong and Yi Zheng and Yu Yang",
title = "An Influence Propagation View of {PageRank}",
journal = j-TKDD,
volume = "11",
number = "3",
pages = "30:1--30:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3046941",
ISSN = "1556-4681 (print), 1556-472X (electronic)",
ISSN-L = "1556-4681",
bibdate = "Mon Jul 24 17:32:52 MDT 2017",
bibsource = "http://www.acm.org/pubs/contents/journals/tkdd/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tkdd.bib",
abstract = "For a long time, PageRank has been widely used for
authority computation and has been adopted as a solid
baseline for evaluating social influence related
applications. However, when measuring the authority of
network nodes, the traditional PageRank method does not
take the nodes' prior knowledge into consideration.
Also, the connection between PageRank and social
influence modeling methods is not clearly established.
To that end, this article provides a focused study on
understanding PageRank as well as the relationship
between PageRank and social influence analysis. Along
this line, we first propose a linear social influence
model and reveal that this model generalizes the
PageRank-based authority computation by introducing
some constraints. Then, we show that the authority
computation by PageRank can be enhanced if exploiting
more reasonable constraints (e.g., from prior
knowledge). Next, to deal with the computational
challenge of linear model with general constraints, we
provide an upper bound for identifying nodes with top
authorities. Moreover, we extend the proposed linear
model for better measuring the authority of the given
node sets, and we also demonstrate the way to quickly
identify the top authoritative node sets. Finally,
extensive experimental evaluations on four real-world
networks validate the effectiveness of the proposed
linear model with respect to different constraint
settings. The results show that the methods with more
reasonable constraints can lead to better ranking and
recommendation performance. Meanwhile, the upper bounds
formed by PageRank values could be used to quickly
locate the nodes and node sets with the highest
authorities.",
acknowledgement = ack-nhfb,
articleno = "30",
fjournal = "ACM Transactions on Knowledge Discovery from Data
(TKDD)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1054",
}
@Article{Pradhan:2017:CIP,
author = "Dinesh Pradhan and Partha Sarathi Paul and Umesh
Maheswari and Subrata Nandi and Tanmoy Chakraborty",
title = "{$ C^3 $}-index: a {PageRank} based multi-faceted
metric for authors' performance measurement",
journal = j-SCIENTOMETRICS,
volume = "110",
number = "1",
pages = "253--273",
month = jan,
year = "2017",
CODEN = "SCNTDX",
DOI = "https://doi.org/10.1007/s11192-016-2168-y",
ISSN = "0138-9130 (print), 1588-2861 (electronic)",
ISSN-L = "0138-9130",
bibdate = "Mon Jan 30 06:44:49 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
URL = "http://link.springer.com/accesspage/article/10.1007/s11192-016-2168-y",
acknowledgement = ack-nhfb,
fjournal = "Scientometrics",
journal-URL = "http://link.springer.com/journal/11192",
}
@Article{Rafailidis:2017:LSS,
author = "D. Rafailidis and E. Constantinou and Y.
Manolopoulos",
title = "Landmark selection for spectral clustering based on
{Weighted PageRank}",
journal = j-FUT-GEN-COMP-SYS,
volume = "68",
number = "??",
pages = "465--472",
month = mar,
year = "2017",
CODEN = "FGSEVI",
ISSN = "0167-739X (print), 1872-7115 (electronic)",
ISSN-L = "0167-739X",
bibdate = "Sat Dec 10 08:32:13 MST 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/futgencompsys.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0167739X16300504",
acknowledgement = ack-nhfb,
fjournal = "Future Generation Computer Systems",
journal-URL = "http://www.sciencedirect.com/science/journal/0167739X/",
}
@Article{Reinstaller:2017:UPA,
author = "Andreas Reinstaller and Peter Reschenhofer",
title = "Using {PageRank} in the analysis of technological
progress through patents: an illustration for
biotechnological inventions",
journal = j-SCIENTOMETRICS,
volume = "113",
number = "3",
pages = "1407--1438",
month = dec,
year = "2017",
CODEN = "SCNTDX",
DOI = "https://doi.org/10.1007/s11192-017-2549-x",
ISSN = "0138-9130 (print), 1588-2861 (electronic)",
ISSN-L = "0138-9130",
bibdate = "Tue Nov 21 07:25:48 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
URL = "http://link.springer.com/article/10.1007/s11192-017-2549-x",
acknowledgement = ack-nhfb,
fjournal = "Scientometrics",
journal-URL = "http://link.springer.com/journal/11192",
}
@Article{Shao:2017:DSA,
author = "Fei Shao and Rong Peng and Han Lai and Bangchao Wang",
title = "{DRank}: a semi-automated requirements prioritization
method based on preferences and dependencies",
journal = j-J-SYST-SOFTW,
volume = "126",
number = "??",
pages = "141--156",
month = apr,
year = "2017",
CODEN = "JSSODM",
DOI = "https://doi.org/10.1016/j.jss.2016.09.043",
ISSN = "0164-1212 (print), 1873-1228 (electronic)",
ISSN-L = "0164-1212",
bibdate = "Fri Feb 10 10:22:09 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jsystsoftw.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0164121216301911",
acknowledgement = ack-nhfb,
fjournal = "Journal of Systems and Software",
journal-URL = "http://www.sciencedirect.com/science/journal/01641212/",
keywords = "DRank; PageRank-Req; Prioritization Evaluation
Attributes Tree (PEAT)",
}
@Article{Shen:2017:EES,
author = "Zhao-Li Shen and Ting-Zhu Huang and Bruno Carpentieri
and Xian-Ming Gu and Chun Wen",
title = "An efficient elimination strategy for solving
{PageRank} problems",
journal = j-APPL-MATH-COMP,
volume = "298",
number = "??",
pages = "111--122",
day = "1",
month = apr,
year = "2017",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2016.10.031",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Fri Dec 23 12:38:50 MST 2016",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300316306385",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003/",
}
@Article{Tan:2017:NEM,
author = "Xueyuan Tan",
title = "A new extrapolation method for {PageRank}
computations",
journal = j-J-COMPUT-APPL-MATH,
volume = "313",
number = "??",
pages = "383--392",
day = "15",
month = mar,
year = "2017",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:36:49 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042716304034",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Wen:2017:NTS,
author = "Chun Wen and Ting-Zhu Huang and Zhao-Li Shen",
title = "A note on the two-step matrix splitting iteration for
computing {PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "315",
number = "??",
pages = "87--97",
day = "1",
month = may,
year = "2017",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 13:36:50 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S037704271630509X",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Avrachenkov:2018:MFA,
author = "Konstantin Avrachenkov and Arun Kadavankandy and Nelly
Litvak",
title = "Mean Field Analysis of Personalized {PageRank} with
Implications for Local Graph Clustering",
journal = j-J-STAT-PHYS,
volume = "173",
number = "3--4",
pages = "895--916",
month = nov,
year = "2018",
CODEN = "JSTPSB",
DOI = "https://doi.org/10.1007/s10955-018-2099-5",
ISSN = "0022-4715 (print), 1572-9613 (electronic)",
ISSN-L = "0022-4715",
bibdate = "Fri Mar 1 07:23:16 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jstatphys2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Journal of Statistical Physics",
journal-URL = "http://link.springer.com/journal/10955",
}
@Article{Boldi:2018:BMC,
author = "Paolo Boldi and Andrea Marino and Massimo Santini and
Sebastiano Vigna",
title = "{BUbiNG}: Massive Crawling for the Masses",
journal = j-TWEB,
volume = "12",
number = "2",
pages = "12:1--12:26",
month = jun,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3160017",
ISSN = "1559-1131 (print), 1559-114X (electronic)",
ISSN-L = "1559-1131",
bibdate = "Thu Jun 28 14:10:01 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/java2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tweb.bib",
URL = "https://dl.acm.org/citation.cfm?doid=3176641.3160017",
abstract = "Although web crawlers have been around for twenty
years by now, there is virtually no freely available,
open-source crawling software that guarantees high
throughput, overcomes the limits of single-machine
systems, and, at the same time, scales linearly with
the amount of resources available. This article aims at
filling this gap, through the description of BUbiNG,
our next-generation web crawler built upon the authors'
experience with UbiCrawler [9] and on the last ten
years of research on the topic. BUbiNG is an
open-source Java fully distributed crawler; a single
BUbiNG agent, using sizeable hardware, can crawl
several thousand pages per second respecting strict
politeness constraints, both host- and IP-based. Unlike
existing open-source distributed crawlers that rely on
batch techniques (like MapReduce), BUbiNG job
distribution is based on modern high-speed protocols to
achieve very high throughput.",
acknowledgement = ack-nhfb,
articleno = "12",
fjournal = "ACM Transactions on the Web (TWEB)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1062",
keywords = "BUbiNG; centrality measures; distributed systems;
Java; PageRank; UbiCrawler; Web crawling",
}
@Article{Cui:2018:UDR,
author = "Yi Cui and Clint Sparkman and Hsin-Tsang Lee and
Dmitri Loguinov",
title = "Unsupervised Domain Ranking in Large-Scale {Web}
Crawls",
journal = j-TWEB,
volume = "12",
number = "4",
pages = "26:1--26:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3182180",
ISSN = "1559-1131 (print), 1559-114X (electronic)",
ISSN-L = "1559-1131",
bibdate = "Tue Oct 22 08:10:06 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tweb.bib",
abstract = "With the proliferation of web spam and infinite
autogenerated web content, large-scale web crawlers
require low-complexity ranking methods to effectively
budget their limited resources and allocate bandwidth
to reputable sites. In this work, we assume crawls that
produce frontiers orders of magnitude larger than RAM,
where sorting of pending URLs is infeasible in real
time. Under these constraints, the main objective is to
quickly compute domain budgets and decide which of them
can be massively crawled. Those ranked at the top of
the list receive aggressive crawling allowances, while
all other domains are visited at some small default
rate. To shed light on Internet-wide spam avoidance, we
study topology-based ranking algorithms on domain-level
graphs from the two largest academic crawls: a
6.3B-page IRLbot dataset and a 1B-page ClueWeb09
exploration. We first propose a new methodology for
comparing the various rankings and then show that
in-degree BFS-based techniques decisively outperform
classic PageRank-style methods, including TrustRank.
However, since BFS requires several orders of magnitude
higher overhead and is generally infeasible for
real-time use, we propose a fast, accurate, and
scalable estimation method called TSE that can achieve
much better crawl prioritization in practice. It is
especially beneficial in applications with limited
hardware resources.",
acknowledgement = ack-nhfb,
articleno = "26",
fjournal = "ACM Transactions on the Web (TWEB)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1062",
}
@Article{Gu:2018:GPA,
author = "Chuanqing Gu and Xianglong Jiang and Chenchen Shao and
Zhibing Chen",
title = "A {GMRES-Power} algorithm for computing {PageRank}
problems",
journal = j-J-COMPUT-APPL-MATH,
volume = "343",
number = "??",
pages = "113--123",
day = "1",
month = dec,
year = "2018",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2018.03.017",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Fri Aug 10 18:10:42 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042718301638",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Gu:2018:PMS,
author = "Chuanqing Gu and Xianglong Jiang and Ying Nie and
Zhibing Chen",
title = "A preprocessed multi-step splitting iteration for
computing {PageRank}",
journal = j-APPL-MATH-COMP,
volume = "338",
number = "??",
pages = "72--86",
day = "1",
month = dec,
year = "2018",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2018.05.033",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Fri Sep 14 08:14:14 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300318304429",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003",
}
@Article{Ikegami:2018:PTM,
author = "Kenshin Ikegami and Yukio Ohsawa",
title = "{PageRank} Topic Model: Estimation of Multinomial
Distributions using Network Structure Analysis
Methods",
journal = j-FUND-INFO,
volume = "159",
number = "3",
pages = "257--277",
month = "????",
year = "2018",
CODEN = "FUMAAJ",
DOI = "https://doi.org/10.3233/FI-2018-1664",
ISSN = "0169-2968 (print), 1875-8681 (electronic)",
ISSN-L = "0169-2968",
bibdate = "Fri Sep 21 07:16:40 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/fundinfo2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Fundamenta Informaticae",
journal-URL = "http://content.iospress.com/journals/fundamenta-informaticae",
}
@Article{Meini:2018:PBA,
author = "Beatrice Meini and Federico Poloni",
title = "{Perron}-based algorithms for the multilinear
{PageRank}",
journal = j-NUM-LIN-ALG-APPL,
volume = "25",
number = "6",
pages = "??--??",
month = dec,
year = "2018",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.2177",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
ISSN-L = "1070-5325",
bibdate = "Tue Jan 29 12:09:28 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
articleno = "e2177",
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "http://www3.interscience.wiley.com/cgi-bin/jhome/5957",
onlinedate = "16 April 2018",
}
@Article{Mendes:2018:PCM,
author = "I. R. Mendes and P. B. Vasconcelos",
title = "{PageRank} Computation with {MAAOR} and Lumping
Methods",
journal = j-MATH-COMPUT-SCI,
volume = "12",
number = "2",
pages = "129--141",
month = jun,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1007/s11786-018-0335-7",
ISSN = "1661-8270 (print), 1661-8289 (electronic)",
ISSN-L = "1661-8270",
bibdate = "Mon Mar 4 06:59:44 MST 2019",
bibsource = "http://link.springer.com/journal/11786/12/2;
https://www.math.utah.edu/pub/tex/bib/math-comput-sci.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Mathematics in Computer Science",
journal-URL = "http://link.springer.com/journal/11786",
}
@Article{Miyata:2018:HSA,
author = "Takafumi Miyata",
title = "A heuristic search algorithm based on subspaces for
{PageRank} computation",
journal = j-J-SUPERCOMPUTING,
volume = "74",
number = "7",
pages = "3278--3294",
month = jul,
year = "2018",
CODEN = "JOSUED",
DOI = "https://doi.org/10.1007/s11227-018-2383-9",
ISSN = "0920-8542 (print), 1573-0484 (electronic)",
ISSN-L = "0920-8542",
bibdate = "Thu Oct 10 15:31:13 MDT 2019",
bibsource = "http://link.springer.com/journal/11227/74/7;
https://www.math.utah.edu/pub/tex/bib/jsuper.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "The Journal of Supercomputing",
journal-URL = "http://link.springer.com/journal/11227",
}
@Article{Pedroche:2018:SEP,
author = "Francisco Pedroche and Esther Garc{\'\i}a and Miguel
Romance and Regino Criado",
title = "Sharp estimates for the personalized Multiplex
{PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "330",
number = "??",
pages = "1030--1040",
day = "1",
month = mar,
year = "2018",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Fri Jan 12 08:18:04 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042717300717",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Pedroche:2018:STL,
author = "Francisco Pedroche and Esther Garc{\'\i}a and Miguel
Romance and Regino Criado",
title = "On the spectrum of two-layer approach and {Multiplex
PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "344",
number = "??",
pages = "161--172",
day = "15",
month = dec,
year = "2018",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2018.05.033",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Fri Aug 10 18:10:43 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042718303042",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Zhang:2018:CTP,
author = "Yongjun Zhang and Jialin Ma and Zijian Wang and Bolun
Chen and Yongtao Yu",
title = "Collective topical {PageRank}: a model to evaluate the
topic-dependent academic impact of scientific papers",
journal = j-SCIENTOMETRICS,
volume = "114",
number = "3",
pages = "1345--1372",
month = mar,
year = "2018",
CODEN = "SCNTDX",
DOI = "https://doi.org/10.1007/s11192-017-2626-1",
ISSN = "0138-9130 (print), 1588-2861 (electronic)",
ISSN-L = "0138-9130",
bibdate = "Wed Feb 21 15:50:41 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
URL = "http://link.springer.com/article/10.1007/s11192-017-2626-1",
acknowledgement = ack-nhfb,
fjournal = "Scientometrics",
journal-URL = "http://link.springer.com/journal/11192",
}
@Article{Zhang:2018:SRI,
author = "Ziqi Zhang and Jie Gao and Fabio Ciravegna",
title = "{SemRe-Rank}: Improving Automatic Term Extraction by
Incorporating Semantic Relatedness with Personalised
{PageRank}",
journal = j-TKDD,
volume = "12",
number = "5",
pages = "57:1--57:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3201408",
ISSN = "1556-4681 (print), 1556-472X (electronic)",
ISSN-L = "1556-4681",
bibdate = "Tue Jan 29 17:18:46 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tkdd.bib",
abstract = "Automatic Term Extraction (ATE) deals with the
extraction of terminology from a domain specific
corpus, and has long been an established research area
in data and knowledge acquisition. ATE remains a
challenging task as it is known that there is no
existing ATE methods that can consistently outperform
others in any domain. This work adopts a refreshed
perspective to this problem: instead of searching for
such a `one-size-fit-all' solution that may never
exist, we propose to develop generic methods to
`enhance' existing ATE methods. We introduce
SemRe-Rank, the first method based on this principle,
to incorporate semantic relatedness-an often overlooked
venue-into an existing ATE method to further improve
its performance. SemRe-Rank incorporates word
embeddings into a personalised PageRank process to
compute `semantic importance' scores for candidate
terms from a graph of semantically related words
(nodes), which are then used to revise the scores of
candidate terms computed by a base ATE algorithm.
Extensively evaluated with 13 state-of-the-art base ATE
methods on four datasets of diverse nature, it is shown
to have achieved widespread improvement over all base
methods and across all datasets, with up to 15
percentage points when measured by the Precision in the
top ranked K candidate terms (the average for a set of
K 's), or up to 28 percentage points in F1 measured at
a K that equals to the expected real terms in the
candidates (F1 in short). Compared to an alternative
approach built on the well-known TextRank algorithm,
SemRe-Rank can potentially outperform by up to 8 points
in Precision at top K, or up to 17 points in F1.",
acknowledgement = ack-nhfb,
articleno = "57",
fjournal = "ACM Transactions on Knowledge Discovery from Data
(TKDD)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1054",
}
@Article{Zheng:2018:ESG,
author = "Long Zheng and Xiaofei Liao and Hai Jin",
title = "Efficient and Scalable Graph Parallel Processing With
Symbolic Execution",
journal = j-TACO,
volume = "15",
number = "1",
pages = "3:1--3:??",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3170434",
ISSN = "1544-3566 (print), 1544-3973 (electronic)",
ISSN-L = "1544-3566",
bibdate = "Tue Jan 8 17:19:59 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/taco.bib",
abstract = "Existing graph processing essentially relies on the
underlying iterative execution with synchronous (Sync)
and/or asynchronous (Async) engine. Nevertheless, they
both suffer from a wide class of inherent serialization
arising from data interdependencies within a graph. In
this article, we present SymGraph, a judicious graph
engine with symbolic iteration that enables the
parallelism of dependent computation on vertices.
SymGraph allows using abstract symbolic value (instead
of the concrete value) for the computation if the
desired data is unavailable. To maximize the potential
of symbolic iteration, we propose a chain of tailored
sophisticated techniques, enabling SymGraph to scale
out with a new milestone of efficiency for large-scale
graph processing. We evaluate SymGraph in comparison to
Sync, Async, and a hybrid of Sync and Async engines.
Our results on 12 nodes show that SymGraph outperforms
all three graph engines by 1.93x (vs. Sync), 1.98x (vs.
Async), and 1.57x (vs. Hybrid) on average. In
particular, the performance for PageRank on 32 nodes
can be dramatically improved by 16.5x (vs. Sync), 23.3x
(vs. Async), and 12.1x (vs. Hybrid), respectively. The
efficiency of SymGraph is also validated with at least
one order of magnitude improvement in contrast to three
specialized graph systems (Naiad, GraphX, and PGX.D).",
acknowledgement = ack-nhfb,
articleno = "3",
fjournal = "ACM Transactions on Architecture and Code Optimization
(TACO)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J924",
}
@Article{Arrigo:2019:NBP,
author = "Francesca Arrigo and Desmond J. Higham and Vanni
Noferini",
title = "Non-backtracking {PageRank}",
journal = j-J-SCI-COMPUT,
volume = "80",
number = "3",
pages = "1419--1437",
month = sep,
year = "2019",
CODEN = "JSCOEB",
DOI = "https://doi.org/10.1007/s10915-019-00981-8",
ISSN = "0885-7474 (print), 1573-7691 (electronic)",
ISSN-L = "0885-7474",
bibdate = "Thu May 13 07:27:54 MDT 2021",
bibsource = "http://link.springer.com/journal/10915/80/3;
https://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s10915-019-00981-8;
https://link.springer.com/content/pdf/10.1007/s10915-019-00981-8.pdf",
acknowledgement = ack-nhfb,
fjournal = "Journal of Scientific Computing",
journal-URL = "http://link.springer.com/journal/10915",
}
@InCollection{DiBucchianico:2019:MBD,
author = "Alessandro {Di Bucchianico} and Laura Iapichino and
Nelly Litvak and Frank van der Meulen and Ron Wehrens",
title = "Mathematics for big data",
crossref = "Pitici:2019:BWM",
pages = "120--131",
year = "2019",
bibdate = "Mon Mar 16 15:45:15 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
keywords = "PageRank; Web data analytics",
}
@Article{Makkar:2019:CSF,
author = "Aaisha Makkar and Neeraj Kumar",
title = "Cognitive spammer: a Framework for {PageRank} analysis
with Split by Over-sampling and Train by
Under-fitting",
journal = j-FUT-GEN-COMP-SYS,
volume = "90",
number = "??",
pages = "381--404",
month = jan,
year = "2019",
CODEN = "FGSEVI",
DOI = "https://doi.org/10.1016/j.future.2018.07.046",
ISSN = "0167-739X (print), 1872-7115 (electronic)",
ISSN-L = "0167-739X",
bibdate = "Tue Sep 18 14:07:59 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/futgencompsys.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0167739X18305703",
acknowledgement = ack-nhfb,
fjournal = "Future Generation Computer Systems",
journal-URL = "http://www.sciencedirect.com/science/journal/0167739X",
}
@Article{Massucci:2019:MAR,
author = "Francesco Alessandro Massucci and Domingo Docampo",
title = "Measuring the academic reputation through citation
networks via {PageRank}",
journal = j-J-INFORMETRICS,
volume = "13",
number = "1",
pages = "185--201",
month = feb,
year = "2019",
CODEN = "????",
ISSN = "1751-1577 (print), 1875-5879 (electronic)",
ISSN-L = "1751-1577",
bibdate = "Fri Feb 5 16:33:15 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S175115771830110X",
acknowledgement = ack-nhfb,
fjournal = "Journal of Informetrics",
journal-URL = "http://www.sciencedirect.com/science/journal/17511577/",
}
@Article{Robertson:2019:BHS,
author = "Stephen Robertson",
title = "A Brief History of Search Results Ranking",
journal = j-IEEE-ANN-HIST-COMPUT,
volume = "41",
number = "2",
pages = "22--28",
month = apr,
year = "2019",
CODEN = "IAHCEX",
DOI = "https://doi.org/10.1109/MAHC.2019.2897559",
ISSN = "1058-6180 (print), 1934-1547 (electronic)",
ISSN-L = "1058-6180",
bibdate = "Mon Jul 8 07:40:56 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ieeeannhistcomput.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE Annals of the History of Computing",
journal-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=85",
keywords = "alternative methods; brief history; extensive research
work; History; Indexing; Information retrieval;
Internet; JACM paper; learning (artificial
intelligence); learning methods; ranking systems;
Rankng (statistics); search engines; Search methods;
search results; twentieth century; Web search; web
search engines",
remark = "See \url{https://history.computer.org/annals/dtp/} for
additional notes, corrections, interviews, and
photographs.",
}
@Article{Shen:2019:DLR,
author = "Zhao-Li Shen and Ting-Zhu Huang and Bruno Carpentieri
and Chun Wen and Xian-Ming Gu and Xue-Yuan Tan",
title = "Off-diagonal low-rank preconditioner for difficult
{PageRank} problems",
journal = j-J-COMPUT-APPL-MATH,
volume = "346",
number = "??",
pages = "456--470",
day = "15",
month = jan,
year = "2019",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2018.07.015",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Mon Mar 18 11:19:57 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042718304357",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Shi:2019:RTP,
author = "Jieming Shi and Renchi Yang and Tianyuan Jin and
Xiaokui Xiao and Yin Yang",
title = "Realtime top-$k$ {Personalized PageRank} over large
graphs on {GPUs}",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "13",
number = "1",
pages = "15--28",
month = sep,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.14778/3357377.3357379",
ISSN = "2150-8097",
bibdate = "Wed Oct 2 06:49:03 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
abstract = "Given a graph G, a source node s \in G and a positive
integer k, a top- k Personalized PageRank (PPR) query
returns the k nodes with the highest PPR values with
respect to s, where the PPR of a node v measures its
relevance from the perspective of source s. Top- k PPR
processing is a fundamental task in many important
applications such as web search, social networks, and
graph analytics. This paper aims to answer such a query
in realtime, i.e., within less than 100ms, on an
Internet-scale graph with billions of edges. This is
far beyond the current state of the art, due to the
immense computational cost of processing a PPR query.
We achieve this goal with a novel algorithm kPAR, which
utilizes the massive parallel processing power of GPUs.
The main challenge in designing a GPU-based PPR
algorithm lies in that a GPU is mainly a parallel
computation device, whereas PPR processing involves
graph traversals and value propagation operations,
which are inherently sequential and memory-bound.
Existing scalable PPR algorithms are mostly described
as single-thread CPU solutions that are resistant to
parallelization. Further, they usually involve complex
data structures which do not have efficient adaptations
on GPUs. kPAR overcomes these problems via both novel
algorithmic designs (namely, adaptive forward push and
inverted random walks ) and system engineering (e.g.,
load balancing) to realize the potential of GPUs.
Meanwhile, kPAR provides rigorous guarantees on both
result quality and worst-case efficiency. Extensive
experiments show that kPAR is usually 10x faster than
parallel adaptations of existing methods. Notably, on a
billion-edge Twitter graph, kPAR answers a top-1000 PPR
query in 42.4 milliseconds.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1174",
}
@Article{Tian:2019:GIO,
author = "Zhaolu Tian and Yong Liu and Yan Zhang and Zhongyun
Liu and Maoyi Tian",
title = "The general inner-outer iteration method based on
regular splittings for the {PageRank} problem",
journal = j-APPL-MATH-COMP,
volume = "356",
number = "??",
pages = "479--501",
day = "1",
month = sep,
year = "2019",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2019.02.066",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Wed May 15 07:15:42 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300319301766",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003",
}
@Article{Vial:2019:RCP,
author = "Daniel Vial and Vijay Subramanian",
title = "On the Role of Clustering in Personalized {PageRank}
Estimation",
journal = j-TOMPECS,
volume = "4",
number = "4",
pages = "21:1--21:33",
month = dec,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3366635",
ISSN = "2376-3639 (print), 2376-3647 (electronic)",
ISSN-L = "2376-3639",
bibdate = "Thu Mar 19 13:56:10 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tompecs.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3366635",
abstract = "Personalized PageRank (PPR) is a measure of the
importance of a node from the perspective of another
(we call these nodes the target and the source,
respectively). PPR has been used in many applications,
such as offering a Twitter user (the source)
recommendations of whom to follow (targets deemed
important by PPR); additionally, PPR has been used in
graph-theoretic problems such as community detection.
However, computing PPR is infeasible for large networks
like Twitter, so efficient estimation algorithms are
necessary.\par
In this work, we analyze the relationship between PPR
estimation complexity and clustering. First, we devise
algorithms to estimate PPR for many source/target
pairs. In particular, we propose an enhanced version of
the existing single pair estimator Bidirectional-PPR
that is more useful as a primitive for many pair
estimation. We then show that the common underlying
graph can be leveraged to efficiently and jointly
estimate PPR for many pairs rather than treating each
pair separately using the primitive algorithm. Next, we
show the complexity of our joint estimation scheme
relates closely to the degree of clustering among the
sources and targets at hand, indicating that estimating
PPR for many pairs is easier when clustering occurs.
Finally, we consider estimating PPR when several
machines are available for parallel computation,
devising a method that leverages our clustering
findings, specifically the quantities computed in situ,
to assign tasks to machines in a manner that reduces
computation time. This demonstrates that the
relationship between complexity and clustering has
important consequences in a practical distributed
setting.",
acknowledgement = ack-nhfb,
articleno = "21",
fjournal = "ACM Transactions on Modeling and Performance
Evaluation of Computing Systems (TOMPECS)",
journal-URL = "https://dl.acm.org/loi/tompecs",
}
@Article{Vial:2019:SRP,
author = "Daniel Vial and Vijay Subramanian",
title = "A Structural Result for Personalized {PageRank} and
its Algorithmic Consequences",
journal = j-SIGMETRICS,
volume = "47",
number = "1",
pages = "39--40",
month = dec,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3376930.3376956",
ISSN = "0163-5999 (print), 1557-9484 (electronic)",
ISSN-L = "0163-5999",
bibdate = "Mon Jan 27 06:15:26 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/sigmetrics.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3376930.3376956",
abstract = "Many natural and man-made systems can be represented
as graphs, sets of objects (called nodes) and pairwise
relations between these objects (called edges). These
include the brain, which contains neurons (nodes) that
exchange signals through chemical \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "ACM SIGMETRICS Performance Evaluation Review",
journal-URL = "https://dl.acm.org/loi/sigmetrics",
}
@Article{Wang:2019:EAA,
author = "Sibo Wang and Renchi Yang and Runhui Wang and Xiaokui
Xiao and Zhewei Wei and Wenqing Lin and Yin Yang and
Nan Tang",
title = "Efficient Algorithms for Approximate Single-Source
Personalized {PageRank} Queries",
journal = j-TODS,
volume = "44",
number = "4",
pages = "18:1--18:??",
month = oct,
year = "2019",
CODEN = "ATDSD3",
DOI = "https://doi.org/10.1145/3360902",
ISSN = "0362-5915 (print), 1557-4644 (electronic)",
ISSN-L = "0362-5915",
bibdate = "Tue Oct 29 10:55:21 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tods.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3360902",
abstract = "Given a graph G, a source node s, and a target node t,
the personalized PageRank ( PPR ) of t with respect to
s is the probability that a random walk starting from s
terminates at t. An important variant of the PPR query
is single-source PPR ( SSPPR ), which enumerates all
nodes in G and returns the top- k nodes with the
highest PPR values with respect to a given source s.
PPR in general and SSPPR in particular have important
applications in web search and social networks, e.g.,
in Twitter's Who-To-Follow recommendation service.
However, PPR computation is known to be expensive on
large graphs and resistant to indexing. Consequently,
previous solutions either use heuristics, which do not
guarantee result quality, or rely on the strong
computing power of modern data centers, which is
costly. Motivated by this, we propose effective
index-free and index-based algorithms for approximate
PPR processing, with rigorous guarantees on result
quality. We first present FORA, an approximate SSPPR
solution that combines two existing methods-Forward
Push (which is fast but does not guarantee quality) and
Monte Carlo Random Walk (accurate but slow)-in a simple
and yet non-trivial way, leading to both high accuracy
and efficiency. Further, FORA includes a simple and
effective indexing scheme, as well as a module for top-
k selection with high pruning power. Extensive
experiments demonstrate that the proposed solutions are
orders of magnitude more efficient than their
respective competitors. Notably, on a billion-edge
Twitter dataset, FORA answers a top-500 approximate
SSPPR query within 1s, using a single commodity
server.",
acknowledgement = ack-nhfb,
articleno = "18",
fjournal = "ACM Transactions on Database Systems",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J777",
}
@Article{Wang:2019:PAS,
author = "Runhui Wang and Sibo Wang and Xiaofang Zhou",
title = "Parallelizing approximate single-source personalized
{PageRank} queries on shared memory",
journal = j-VLDB-J,
volume = "28",
number = "6",
pages = "923--940",
month = dec,
year = "2019",
CODEN = "VLDBFR",
DOI = "https://doi.org/10.1007/s00778-019-00576-7",
ISSN = "1066-8888 (print), 0949-877X (electronic)",
ISSN-L = "1066-8888",
bibdate = "Thu Mar 19 17:10:21 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
URL = "http://link.springer.com/article/10.1007/s00778-019-00576-7",
acknowledgement = ack-nhfb,
fjournal = "VLDB Journal: Very Large Data Bases",
journal-URL = "http://portal.acm.org/toc.cfm?id=J869",
}
@Article{Yao:2019:TBR,
author = "Xin Yao and Yizhu Zou and Zhigang Chen and Ming Zhao
and Qin Liu",
title = "Topic-based rank search with verifiable social data
outsourcing",
journal = j-J-PAR-DIST-COMP,
volume = "134",
number = "??",
pages = "1--12",
month = dec,
year = "2019",
CODEN = "JPDCER",
ISSN = "0743-7315 (print), 1096-0848 (electronic)",
ISSN-L = "0743-7315",
bibdate = "Wed Mar 18 09:26:10 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jpardistcomp.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0743731519300322",
acknowledgement = ack-nhfb,
fjournal = "Journal of Parallel and Distributed Computing",
journal-URL = "http://www.sciencedirect.com/science/journal/07437315",
}
@Article{Yu:2019:EPP,
author = "Weiren Yu and Julie McCann and Chengyuan Zhang",
title = "Efficient Pairwise Penetrating-rank Similarity
Retrieval",
journal = j-TWEB,
volume = "13",
number = "4",
pages = "21:1--21:??",
month = dec,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3368616",
ISSN = "1559-1131 (print), 1559-114X (electronic)",
ISSN-L = "1559-1131",
bibdate = "Sat Dec 21 07:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tweb.bib",
abstract = "Many web applications demand a measure of similarity
between two entities, such as collaborative filtering,
web document ranking, linkage prediction, and anomaly
detection. P-Rank (Penetrating-Rank) has been accepted
as a promising graph-based similarity measure, as it
provides a comprehensive way of encoding both incoming
and outgoing links into assessment. However, the
existing method to compute P-Rank is iterative in
nature and rather cost-inhibitive. Moreover, the
accuracy estimate and stability issues for P-Rank
computation have not been addressed. In this article,
we consider the optimization techniques for P-Rank
search that encompasses its accuracy, stability, and
computational efficiency. (1) The accuracy estimation
is provided for P-Rank iterations, with the aim to find
out the number of iterations, $k$, required to
guarantee a desired accuracy. (2) A rigorous bound on
the condition number of P-Rank is obtained for
stability analysis. Based on this bound, it can be
shown that P-Rank is stable and well-conditioned when
the damping factors are chosen to be suitably small.
(3) Two matrix-based algorithms, applicable to digraphs
and undirected graphs, are, respectively, devised for
efficient P-Rank computation, which improves the
computational time from $ O(k n^3) $ to $ O(\upsilon
n^2 + \upsilon^6) $ for digraphs, and to $ O(\upsilon
n^2) $ for undirected graphs, where $n$ is the number
of vertices in the graph, and $ \upsilon (\ll n)$ is
the target rank of the graph. Moreover, our proposed
algorithms can significantly reduce the memory space of
P-Rank computations from $ O(n^2) $ to $ O(\upsilon n +
\upsilon^4) $ for digraphs, and to $ O(\upsilon n) $
for undirected graphs, respectively. Finally, extensive
experiments on real-world and synthetic datasets
demonstrate the usefulness and efficiency of the
proposed techniques for P-Rank similarity assessment on
various networks.",
acknowledgement = ack-nhfb,
articleno = "21",
fjournal = "ACM Transactions on the Web (TWEB)",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1062",
}
@Article{Chen:2020:TSM,
author = "Fan Chen and Yini Zhang and Karl Rohe",
title = "Targeted sampling from massive block model graphs with
personalized {PageRank}",
journal = j-J-R-STAT-SOC-SER-B-STAT-METHODOL,
volume = "82",
number = "1",
pages = "99--126",
month = feb,
year = "2020",
CODEN = "JSTBAJ",
DOI = "https://doi.org/10.1111/rssb.12349",
ISSN = "1369-7412 (print), 1467-9868 (electronic)",
ISSN-L = "1369-7412",
bibdate = "Tue Jul 14 18:37:39 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jrss-b.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
ajournal = "J. R. Stat. Soc., Ser. B Stat. Methodol.",
fjournal = "Journal of the Royal Statistical Society: Series B
(Statistical Methodology)",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9868",
onlinedate = "31 December 2019",
}
@Article{Cipolla:2020:EMF,
author = "Stefano Cipolla and Michela Redivo-Zaglia and
Francesco Tudisco",
title = "Extrapolation methods for fixed-point multilinear
{PageRank} computations",
journal = j-NUM-LIN-ALG-APPL,
volume = "27",
number = "2",
pages = "e2280:1--e2280:??",
month = mar,
year = "2020",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.2280",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
ISSN-L = "1070-5325",
bibdate = "Wed May 27 12:52:44 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "http://www3.interscience.wiley.com/cgi-bin/jhome/5957",
onlinedate = "03 January 2020",
}
@Article{Garavaglia:2020:LWC,
author = "Alessandro Garavaglia and Remco van der Hofstad and
Nelly Litvak",
title = "Local weak convergence for {PageRank}",
journal = j-ANN-APPL-PROBAB,
volume = "30",
number = "1",
pages = "40--79",
month = feb,
year = "2020",
CODEN = "????",
ISSN = "1050-5164 (print), 2168-8737 (electronic)",
ISSN-L = "1050-5164",
bibdate = "Tue Jul 14 17:01:23 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/annapplprobab.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://projecteuclid.org/euclid.aoap/1582621219",
acknowledgement = ack-nhfb,
ajournal = "Ann. Appl. Probab.",
fjournal = "Annals of Applied Probability",
journal-URL = "http://projecteuclid.org/all/euclid.aoap/;
http://www.jstor.org/journals/10505164.html",
}
@Article{Grutzmacher:2020:APC,
author = "Thomas Gr{\"u}tzmacher and Terry Cojean and Goran
Flegar and Hartwig Anzt and Enrique S.
Quintana-Ort{\'\i}",
title = "Acceleration of {PageRank} with Customized Precision
Based on Mantissa Segmentation",
journal = j-TOPC,
volume = "7",
number = "1",
pages = "4:1--4:19",
month = apr,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3380934",
ISSN = "2329-4949 (print), 2329-4957 (electronic)",
ISSN-L = "2329-4949",
bibdate = "Mon Apr 6 08:56:55 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/fparith.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/topc.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3380934",
abstract = "We describe the application of a
communication-reduction technique for the PageRank
algorithm that dynamically adapts the precision of the
data access to the numerical requirements of the
algorithm as the iteration converges. Our
variable-precision strategy, using a customized
precision format based on mantissa segmentation (CPMS),
abandons the IEEE 754 single- and double-precision
number representation formats employed in the standard
implementation of PageRank, and instead handles the
data in memory using a customized floating-point
format. The customized format enables fast data access
in different accuracy, prevents overflow/underflow by
preserving the IEEE 754 double-precision exponent, and
efficiently avoids data duplication, since all bits of
the original IEEE 754 double-precision mantissa are
preserved in memory, but re-organized for efficient
reduced precision access. With this approach, the
truncated values (omitting significand bits), as well
as the original IEEE double-precision values, can be
retrieved without duplicating the data in different
formats.\par
Our numerical experiments on an NVIDIA V100 GPU (Volta
architecture) and a server equipped with two Intel Xeon
Platinum 8168 CPUs (48 cores in total) expose that,
compared with a standard IEEE double-precision
implementation, the CPMS-based PageRank completes about
10\% faster if high-accuracy output is needed, and
about 30\% faster if reduced output accuracy is
acceptable.",
acknowledgement = ack-nhfb,
articleno = "4",
fjournal = "ACM Transactions on Parallel Computing",
journal-URL = "https://dl.acm.org/loi/topc",
}
@Article{Guo:2020:RBE,
author = "Pei-Chang Guo",
title = "A residual-based error bound for the multilinear
{PageRank} vector",
journal = j-LIN-MULT-ALGEBRA,
volume = "68",
number = "3",
pages = "568--574",
year = "2020",
CODEN = "LNMLAZ",
DOI = "https://doi.org/10.1080/03081087.2018.1509937",
ISSN = "0308-1087 (print), 1563-5139 (electronic)",
ISSN-L = "0308-1087",
bibdate = "Mon Mar 9 16:30:36 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/linmultalgebra.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "Linear and Multilinear Algebra",
journal-URL = "http://www.tandfonline.com/loi/glma20",
onlinedate = "17 Aug 2018",
}
@Article{Li:2020:MPU,
author = "Wen Li and Dongdong Liu and Seak-Weng Vong and
Mingqing Xiao",
title = "Multilinear {PageRank}: Uniqueness, error bound and
perturbation analysis",
journal = j-APPL-NUM-MATH,
volume = "156",
number = "??",
pages = "584--607",
month = oct,
year = "2020",
CODEN = "ANMAEL",
ISSN = "0168-9274 (print), 1873-5460 (electronic)",
ISSN-L = "0168-9274",
bibdate = "Tue Dec 29 07:52:53 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applnummath.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0168927420301665",
acknowledgement = ack-nhfb,
fjournal = "Applied Numerical Mathematics: Transactions of IMACS",
journal-URL = "http://www.sciencedirect.com/science/journal/01689274",
}
@Article{Miao:2020:AAM,
author = "Cun-Qiang Miao and Xue-Yuan Tan",
title = "Accelerating the {Arnoldi} method via {Chebyshev}
polynomials for computing {PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "377",
number = "??",
pages = "Article 112891",
day = "15",
month = oct,
year = "2020",
CODEN = "JCAMDI",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Wed May 13 06:58:35 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042720301825",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Sankpal:2020:RRA,
author = "Lata Jaywant Sankpal and Suhas H Patil",
title = "Rider-Rank Algorithm-Based Feature Extraction for
Re-ranking the {Webpages} in the Search Engine",
journal = j-COMP-J,
volume = "63",
number = "10",
pages = "1479--1489",
month = oct,
year = "2020",
CODEN = "CMPJA6",
DOI = "https://doi.org/10.1093/comjnl/bxaa032",
ISSN = "0010-4620 (print), 1460-2067 (electronic)",
ISSN-L = "0010-4620",
bibdate = "Mon Oct 19 08:41:03 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compj2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://academic.oup.com/comjnl/article/63/10/1479/5855737",
acknowledgement = ack-nhfb,
fjournal = "Computer Journal",
journal-URL = "http://comjnl.oxfordjournals.org/",
}
@Article{Shi:2020:RIF,
author = "Jieming Shi and Tianyuan Jin and Renchi Yang and
Xiaokui Xiao and Yin Yang",
title = "Realtime index-free single source {SimRank} processing
on web-scale graphs",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "13",
number = "7",
pages = "966--980",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.14778/3384345.3384347",
ISSN = "2150-8097",
bibdate = "Tue May 5 14:01:13 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
URL = "https://dl.acm.org/doi/abs/10.14778/3384345.3384347",
abstract = "Given a graph $G$ and a node $ u \in G$, a single
source SimRank query evaluates the similarity between
$u$ and every node $ v \in G$. Existing approaches to
single source SimRank computation incur either long
query response time, or expensive pre-computation,
which \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "https://dl.acm.org/loi/pvldb",
}
@Article{Xiao:2020:PRF,
author = "Zhijun Xiao and Cuiping Li and Hong Chen",
title = "{PatternRank+NN}: a Ranking Framework Bringing User
Behaviors into Entity Set Expansion from {Web} Search
Queries",
journal = j-TWEB,
volume = "14",
number = "3",
pages = "10:1--10:15",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3386042",
ISSN = "1559-1131 (print), 1559-114X (electronic)",
ISSN-L = "1559-1131",
bibdate = "Wed Jul 22 17:29:55 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tweb.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3386042",
abstract = "We propose a ranking framework, called PatternRank+NN,
for expanding a set of seed entities of a particular
class (i.e., entity set expansion) from Web search
queries. PatternRank+NN consists of two parts:
PatternRank and NN. Unlike the traditional \ldots{}",
acknowledgement = ack-nhfb,
articleno = "10",
fjournal = "ACM Transactions on the Web (TWEB)",
journal-URL = "https://dl.acm.org/loi/tweb",
}
@Article{Yang:2020:HNE,
author = "Renchi Yang and Jieming Shi and Xiaokui Xiao and Yin
Yang and Sourav S. Bhowmick",
title = "Homogeneous network embedding for massive graphs via
reweighted personalized {PageRank}",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "13",
number = "5",
pages = "670--683",
month = jan,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.14778/3377369.3377376",
ISSN = "2150-8097",
bibdate = "Thu Apr 2 10:51:27 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
URL = "https://dl.acm.org/doi/abs/10.14778/3377369.3377376",
abstract = "Given an input graph G and a node $ v \in G $,
homogeneous network embedding (HNE) maps the graph
structure in the vicinity of $v$ to a compact,
fixed-dimensional feature vector. This paper focuses on
HNE for massive graphs, e.g., with billions of edges.
On \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "https://dl.acm.org/loi/pvldb",
}
@Article{Abadeh:2021:DED,
author = "Maryam Nooraei Abadeh and Mansooreh Mirzaie",
title = "{DiffPageRank}: an efficient differential {PageRank}
approach in {MapReduce}",
journal = j-J-SUPERCOMPUTING,
volume = "77",
number = "1",
pages = "188--211",
month = jan,
year = "2021",
CODEN = "JOSUED",
DOI = "https://doi.org/10.1007/s11227-020-03265-3",
ISSN = "0920-8542 (print), 1573-0484 (electronic)",
ISSN-L = "0920-8542",
bibdate = "Fri May 14 09:19:58 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jsuper.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s11227-020-03265-3",
acknowledgement = ack-nhfb,
fjournal = "The Journal of Supercomputing",
journal-URL = "http://link.springer.com/journal/11227",
online-date = "Published: 30 March 2020 Pages: 188 - 211",
}
@Article{Amodio:2021:IPA,
author = "Pierluigi Amodio and Luigi Brugnano and Filippo
Scarselli",
title = "Implementation of the {PaperRank} and {AuthorRank}
indices in the {Scopus} database",
journal = j-J-INFORMETRICS,
volume = "15",
number = "4",
pages = "Article 101206",
month = nov,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.joi.2021.101206",
ISSN = "1751-1577 (print), 1875-5879 (electronic)",
ISSN-L = "1751-1577",
bibdate = "Thu Mar 10 06:27:37 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S1751157721000778",
acknowledgement = ack-nhfb,
fjournal = "Journal of Informetrics",
journal-URL = "http://www.sciencedirect.com/science/journal/17511577/",
}
@Article{Hou:2021:MPA,
author = "Guanhao Hou and Xingguang Chen and Sibo Wang and
Zhewei Wei",
title = "Massively parallel algorithms for {Personalized
PageRank}",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "14",
number = "9",
pages = "1668--1680",
month = may,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.14778/3461535.3461554",
ISSN = "2150-8097",
bibdate = "Sat Oct 23 06:39:32 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
URL = "https://dl.acm.org/doi/10.14778/3461535.3461554",
abstract = "Personalized PageRank (PPR) has wide applications in
search engines, social recommendations, community
detection, and so on. Nowadays, graphs are becoming
massive and many IT companies need to deal with large
graphs that cannot be fitted into the memory of most
commodity servers. However, most existing
state-of-the-art solutions for PPR computation only
work for single-machines and are inefficient for the
distributed framework since such solutions either (i)
result in an excessively large number of communication
rounds, or (ii) incur high communication costs in each
round.
Motivated by this, we present Delta-Push, an efficient
framework for single-source and top-$k$ PPR queries in
distributed settings. Our goal is to reduce the number
of rounds while guaranteeing that the load, i.e., the
maximum number of messages an executor sends or
receives in a round, can be bounded by the capacity of
each executor. We first present a non-trivial
combination of a redesigned parallel push algorithm and
the Monte-Carlo method to answer single-source PPR
queries. The solution uses pre-sampled random walks to
reduce the number of rounds for the push algorithm.
Theoretical analysis under the Massively Parallel
Computing (MPC) model shows that our proposed solution
bounds the communication rounds to [EQUATION] under a
load of O(m/p), where m is the number of edges of the
input graph, p is the number of executors, and $
\epsilon $ is a user-defined error parameter. In the
meantime, as the number of executors increases to $ p'
= \gamma \cdot p$, the load constraint can be relaxed
since each executor can hold $ O(\gamma \cdot m / p')$
messages with invariant local memory. In such
scenarios, multiple queries can be processed in batches
simultaneously. We show that with a load of $ O(\gamma
\cdot m / p')$, our Delta-Push can process $ \gamma $
queries in a batch with [EQUATION] rounds, while other
baseline solutions still keep the same round cost for
each batch. We further present a new top-$k$ algorithm
that is friendly to the distributed framework and
reduces the number of rounds required in practice.
Extensive experiments show that our proposed solution
is more efficient than alternatives.",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "https://dl.acm.org/loi/pvldb",
}
@Article{Hu:2021:VPA,
author = "Qian-Ying Hu and Chun Wen and Ting-Zhu Huang and
Zhao-Li Shen and Xian-Ming Gu",
title = "A variant of the {Power--Arnoldi} algorithm for
computing {PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "381",
number = "??",
pages = "Article 113034",
day = "1",
month = jan,
year = "2021",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2020.113034",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Mar 27 09:45:44 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042720303253",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Huang:2021:CFP,
author = "Jun Huang and Gang Wu",
title = "Convergence of the fixed-point iteration for
multilinear {PageRank}",
journal = j-NUM-LIN-ALG-APPL,
volume = "28",
number = "5",
pages = "e2379:1--e2379:??",
month = oct,
year = "2021",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.2379",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
ISSN-L = "1070-5325",
bibdate = "Mon Feb 21 13:12:20 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
ajournal = "Num. Lin. Alg. Appl.",
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "https://onlinelibrary.wiley.com/journal/10991506",
onlinedate = "25 March 2021",
}
@Article{Olvera-Cravioto:2021:PBU,
author = "Mariana Olvera-Cravioto",
title = "{PageRank's} behavior under degree correlations",
journal = j-ANN-APPL-PROBAB,
volume = "31",
number = "3",
pages = "1403--1442",
month = jun,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1214/20-AAP1623",
ISSN = "1050-5164 (print), 2168-8737 (electronic)",
ISSN-L = "1050-5164",
MRclass = "05C80; 60J80; 41A60; 60B10",
bibdate = "Wed Apr 6 07:46:07 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/annapplprobab.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://projecteuclid.org/journals/annals-of-applied-probability/volume-31/issue-3/PageRanks-behavior-under-degree-correlations/10.1214/20-AAP1623.full",
acknowledgement = ack-nhfb,
ajournal = "Ann. Appl. Probab.",
fjournal = "Annals of Applied Probability",
journal-URL = "http://projecteuclid.org/all/euclid.aoap/;
http://www.jstor.org/journals/10505164.html",
keywords = "complex networks; degree-correlations; Directed random
graphs; distributional fixed-point equations; PageRank;
power laws; ranking algorithms; Weighted branching
processes",
}
@InProceedings{Pelletier:2021:GJP,
author = "Michel Pelletier and Will Kimmerer and Timothy A.
Davis and Timothy G. Mattson",
editor = "{IEEE}",
booktitle = "{2021 IEEE High Performance Extreme Computing
Conference (HPEC)}",
title = "The {GraphBLAS} in {Julia} and {Python}: the
{PageRank} and Triangle Centralities",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--7",
year = "2021",
DOI = "https://doi.org/10.1109/HPEC49654.2021.9622789",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
acknowledgement = ack-nhfb,
}
@Article{Tian:2021:SRI,
author = "Zhaolu Tian and Yan Zhang and Junxin Wang and
Chuanqing Gu",
title = "Several relaxed iteration methods for computing
{PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "388",
number = "??",
pages = "Article 113295",
day = "1",
month = may,
year = "2021",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2020.113295",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Mar 27 09:45:47 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042720305860",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Tortosa:2021:ARN,
author = "Leandro Tortosa and Jose F. Vicent and Gevorg
Yeghikyan",
title = "An algorithm for ranking the nodes of multiplex
networks with data based on the {PageRank} concept",
journal = j-APPL-MATH-COMP,
volume = "392",
number = "??",
pages = "Article 125676",
day = "1",
month = mar,
year = "2021",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2020.125676",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Sat Mar 13 06:39:51 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300320306299",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003",
}
@Article{Wen:2021:APG,
author = "Chun Wen and Qian-Ying Hu and Guo-Jian Yin and
Xian-Ming Gu and Zhao-Li Shen",
title = "An adaptive {Power--Arnoldi} algorithm for computing
{PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "386",
number = "??",
pages = "Article 113209",
month = apr,
year = "2021",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2020.113209",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Mar 27 09:45:47 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042720305008",
acknowledgement = ack-nhfb,
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Zhang:2021:ESB,
author = "Mengshi Zhang and Yaoxian Li and Xia Li and Lingchao
Chen and Yuqun Zhang and Lingming Zhang and Sarfraz
Khurshid",
title = "An Empirical Study of Boosting Spectrum-Based Fault
Localization via {PageRank}",
journal = j-IEEE-TRANS-SOFTW-ENG,
volume = "47",
number = "6",
pages = "1089--1113",
month = jun,
year = "2021",
CODEN = "IESEDJ",
DOI = "https://doi.org/10.1109/TSE.2019.2911283",
ISSN = "0098-5589 (print), 1939-3520 (electronic)",
ISSN-L = "0098-5589",
bibdate = "Thu Jun 17 08:11:01 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ieeetranssoftweng2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE Transactions on Software Engineering",
journal-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32",
}
@Article{Banerjee:2022:PAD,
author = "Sayan Banerjee and Mariana Olvera-Cravioto",
title = "{PageRank} asymptotics on directed preferential
attachment networks",
journal = j-ANN-APPL-PROBAB,
volume = "32",
number = "4",
pages = "3060--3084",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1214/21-AAP1757",
ISSN = "1050-5164 (print), 2168-8737 (electronic)",
ISSN-L = "1050-5164",
MRclass = "05C80; 60J80; 68P10; 41A60; 60B10",
bibdate = "Wed Mar 22 16:13:27 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/annapplprobab.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://projecteuclid.org/journals/annals-of-applied-probability/volume-32/issue-4/PageRank-asymptotics-on-directed-preferential-attachment-networks/10.1214/21-AAP1757.full",
acknowledgement = ack-nhfb,
ajournal = "Ann. Appl. Probab.",
fjournal = "Annals of Applied Probability",
journal-URL = "http://projecteuclid.org/all/euclid.aoap/;
http://www.jstor.org/journals/10505164.html",
keywords = "complex networks; continuous time branching processes;
directed preferential attachment; Local weak limits;
PageRank; power laws",
}
@Article{Bucci:2022:CMC,
author = "Alberto Bucci and Federico Poloni",
title = "A continuation method for computing the multilinear
{PageRank}",
journal = j-NUM-LIN-ALG-APPL,
volume = "29",
number = "4",
pages = "e2432:1--e2432:??",
month = aug,
year = "2022",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.2432",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
ISSN-L = "1070-5325",
bibdate = "Fri Mar 3 12:16:00 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
ajournal = "Num. Lin. Alg. Appl.",
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "https://onlinelibrary.wiley.com/journal/10991506",
onlinedate = "24 January 2022",
}
@Article{Eedi:2022:IOP,
author = "Hemalatha Eedi and Sahith Karra and Rahul Utkoor",
title = "An Improved\slash Optimized Practical Non-Blocking
{PageRank} Algorithm for Massive Graphs*",
journal = j-INT-J-PARALLEL-PROG,
volume = "50",
number = "3-4",
pages = "381--404",
month = aug,
year = "2022",
CODEN = "IJPPE5",
DOI = "https://doi.org/10.1007/s10766-022-00725-6",
ISSN = "0885-7458 (print), 1573-7640 (electronic)",
ISSN-L = "0885-7458",
bibdate = "Fri Jul 15 17:25:07 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/intjparallelprogram.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s10766-022-00725-6",
acknowledgement = ack-nhfb,
ajournal = "Int. J. Parallel Prog.",
fjournal = "International Journal of Parallel Programming",
journal-URL = "http://link.springer.com/journal/10766",
}
@Article{Gu:2022:HTA,
author = "Xian-Ming Gu and Siu-Long Lei and Bruno Carpentieri",
title = "A {Hessenberg}-type algorithm for computing {PageRank}
Problems",
journal = j-NUMER-ALGORITHMS,
volume = "89",
number = "4",
pages = "1845--1863",
month = apr,
year = "2022",
CODEN = "NUALEG",
DOI = "https://doi.org/10.1007/s11075-021-01175-w",
ISSN = "1017-1398 (print), 1572-9265 (electronic)",
ISSN-L = "1017-1398",
bibdate = "Wed Mar 23 06:29:40 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s11075-021-01175-w",
acknowledgement = ack-nhfb,
ajournal = "Numer. Algorithms",
fjournal = "Numerical Algorithms",
journal-URL = "http://link.springer.com/journal/11075",
}
@Article{Jin:2022:SGA,
author = "Yu Jin and Chun Wen and Zhao-Li Shen and Xian-Ming
Gu",
title = "A simpler {GMRES} algorithm accelerated by {Chebyshev}
polynomials for computing {PageRank}",
journal = j-J-COMPUT-APPL-MATH,
volume = "413",
number = "??",
pages = "??--??",
day = "15",
month = oct,
year = "2022",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2022.114395",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Fri May 27 15:22:59 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042722001819",
acknowledgement = ack-nhfb,
articleno = "114395",
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
@Article{Shen:2022:SPG,
author = "Zhao-Li Shen and Meng Su and Bruno Carpentieri and
Chun Wen",
title = "Shifted power-{GMRES} method accelerated by
extrapolation for solving {PageRank} with multiple
damping factors",
journal = j-APPL-MATH-COMP,
volume = "420",
number = "??",
pages = "Article 126799",
day = "1",
month = may,
year = "2022",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2021.126799",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Mon Jan 31 07:59:07 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S009630032100881X",
acknowledgement = ack-nhfb,
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003",
}
@Article{Tian:2022:CIA,
author = "Zhaolu Tian and Zhongyun Liu and Yinghui Dong",
title = "The coupled iteration algorithms for computing
{PageRank}",
journal = j-NUMER-ALGORITHMS,
volume = "89",
number = "4",
pages = "1603--1637",
month = apr,
year = "2022",
CODEN = "NUALEG",
DOI = "https://doi.org/10.1007/s11075-021-01166-x",
ISSN = "1017-1398 (print), 1572-9265 (electronic)",
ISSN-L = "1017-1398",
bibdate = "Wed Mar 23 06:29:40 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s11075-021-01166-x",
acknowledgement = ack-nhfb,
ajournal = "Numer. Algorithms",
fjournal = "Numerical Algorithms",
journal-URL = "http://link.springer.com/journal/11075",
}
@Article{Wang:2022:EBL,
author = "Hanzhi Wang and Zhewei Wei and Junhao Gan and Ye Yuan
and Xiaoyong Du and Ji-Rong Wen",
title = "Edge-based local push for personalized {PageRank}",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "15",
number = "7",
pages = "1376--1389",
month = mar,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.14778/3523210.3523216",
ISSN = "2150-8097",
bibdate = "Fri Jun 24 09:22:18 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
URL = "https://dl.acm.org/doi/10.14778/3523210.3523216",
abstract = "Personalized PageRank (PPR) is a popular node
proximity metric in graph mining and network research.
A single-source PPR (SSPPR) query asks for the PPR
value of each node on the graph. Due to its importance
and wide applications, decades of efforts have
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "https://dl.acm.org/loi/pvldb",
}
@Article{Zhong:2022:SBC,
author = "Han Zhong and Zheng Li and Peng Chen and Hao Lu and
Yijia Xu",
title = "The selection of burglary cases based on
multidimensional features and {PageRank}",
journal = j-CCPE,
volume = "34",
number = "10",
pages = "e6723:1--e6723:??",
day = "1",
month = may,
year = "2022",
CODEN = "CCPEBO",
DOI = "https://doi.org/10.1002/cpe.6723",
ISSN = "1532-0626 (print), 1532-0634 (electronic)",
ISSN-L = "1532-0626",
bibdate = "Wed Apr 13 09:55:03 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ccpe.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
ajournal = "Concurr. Comput.",
fjournal = "Concurrency and Computation: Practice and Experience",
journal-URL = "http://www.interscience.wiley.com/jpages/1532-0626",
onlinedate = "30 November 2021",
}
@Article{Bowater:2023:EAP,
author = "David Bowater and Emmanuel Stefanakis",
title = "Extending the {Adapted PageRank Algorithm} centrality
model for urban street networks using non-local random
walks",
journal = j-APPL-MATH-COMP,
volume = "446",
number = "??",
pages = "??--??",
day = "1",
month = jun,
year = "2023",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2023.127888",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Thu Feb 23 11:23:36 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300323000577",
acknowledgement = ack-nhfb,
articleno = "127888",
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003",
}
@Article{Carchiolo:2023:ENP,
author = "Vincenza Carchiolo and Marco Grassia and Alessandro
Longheu and Michele Malgeri and Giuseppe Mangioni",
title = "Efficient Node {PageRank} Improvement via
Link-building using Geometric Deep Learning",
journal = j-TKDD,
volume = "17",
number = "3",
pages = "38:1--38:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3551642",
ISSN = "1556-4681 (print), 1556-472X (electronic)",
ISSN-L = "1556-4681",
bibdate = "Fri Mar 31 09:53:45 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tkdd.bib",
URL = "https://dl.acm.org/doi/10.1145/3551642",
abstract = "Centrality is a relevant topic in the field of network
research, due to its various theoretical and practical
implications. In general, all centrality metrics aim at
measuring the importance of nodes (according to some
definition of importance), and such importance scores
are used to rank the nodes in the network, therefore
the rank improvement is a strictly related topic. In a
given network, the rank improvement is achieved by
establishing new links, therefore the question shifts
to which and how many links should be collected to get
a desired rank. This problem, also known as
link-building has been shown to be NP-hard, and most
heuristics developed failed in obtaining good
performance with acceptable computational complexity.
In this article, we present LB--GDM, a novel approach
that leverages Geometric Deep Learning to tackle the
link-building problem. To validate our proposal, 31
real-world networks were considered; tests show that
LB--GDM performs significantly better than the
state-of-the-art heuristics, while having a comparable
or even lower computational complexity, which allows it
to scale well even to large networks.\ldots{}",
acknowledgement = ack-nhfb,
articleno = "38",
fjournal = "ACM Transactions on Knowledge Discovery from Data
(TKDD)",
journal-URL = "https://dl.acm.org/loi/tkdd",
}
@Article{DSilva:2023:ISM,
author = "Jovi D'Silva and Uzzal Sharma",
title = "Impact of Similarity Measures in Graph-based Automatic
Text Summarization of {Konkani} Texts",
journal = j-TALLIP,
volume = "22",
number = "2",
pages = "51:1--51:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3554943",
ISSN = "2375-4699 (print), 2375-4702 (electronic)",
ISSN-L = "2375-4699",
bibdate = "Fri Mar 31 09:33:46 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tallip.bib",
URL = "https://dl.acm.org/doi/10.1145/3554943",
abstract = "Automatic text summarization is a popular area in
Natural Language Processing and Machine Learning. In
this work, we adopt a graph-based text summarization
approach, using PageRank algorithm, for automatically
summarizing Konkani text documents. Konkani, an
Indo--Aryan language spoken primarily in the state of
Goa, which is on the west coast of India. It is a
low-resource language with limited language processing
tools. Such tools are readily available in other
popular languages of choice for automatic text
summarization, like English. The Konkani language
dataset used for this purpose is based on Konkani
folktales. We examine the impact of various
language-independent and language-dependent similarity
measures on the construction of the graph. The
language-dependent similarity measures use pre-trained
fastText word embeddings. A fully connected undirected
graph is constructed for each document with the
sentences represented as the graph's vertices. The
vertices are connected to each other based on how
strongly they are related to one another. Thereafter,
PageRank algorithm is used for ranking the scores of
the vertices. The top-ranking sentences are used to
generate the summary. ROUGE toolkit was used for
evaluating the quality of these system-generated
summaries, and the performance was evaluated against
human generated ``gold-standard'' abstracts and also
compared with baselines and benchmark systems. The
experimental results show that language-independent
similarity measures performed well compared to
language-dependent similarity measures despite not
using language-specific tools, such as stop-words list,
stemming, and word embeddings.",
acknowledgement = ack-nhfb,
articleno = "51",
fjournal = "ACM Transactions on Asian and Low-Resource Language
Information Processing (TALLIP)",
journal-URL = "https://dl.acm.org/loi/tallip",
}
@Article{Huang:2023:TSP,
author = "Jun Huang and Gang Wu",
title = "Truncated and Sparse Power Methods with Partially
Updating for Large and Sparse Higher-Order {PageRank}
Problems",
journal = j-J-SCI-COMPUT,
volume = "95",
number = "1",
pages = "??--??",
month = apr,
year = "2023",
CODEN = "JSCOEB",
DOI = "https://doi.org/10.1007/s10915-023-02146-0",
ISSN = "0885-7474 (print), 1573-7691 (electronic)",
ISSN-L = "0885-7474",
bibdate = "Mon Apr 17 15:38:02 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s10915-023-02146-0",
acknowledgement = ack-nhfb,
ajournal = "J. Sci. Comput.",
articleno = "34",
fjournal = "Journal of Scientific Computing",
journal-URL = "http://link.springer.com/journal/10915",
}
@Article{Lai:2023:AAF,
author = "Fuqi Lai and Wen Li and Xiaofei Peng and Yannan Chen",
title = "{Anderson} accelerated fixed-point iteration for
multilinear {PageRank}",
journal = j-NUM-LIN-ALG-APPL,
volume = "30",
number = "5",
pages = "e2499:1--e2499:??",
month = oct,
year = "2023",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.2499",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
ISSN-L = "1070-5325",
bibdate = "Fri Nov 10 10:09:49 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
ajournal = "Numer. Linear Algebra Appl.",
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "https://onlinelibrary.wiley.com/journal/10991506",
onlinedate = "28 March 2023",
}
@Article{Li:2023:ZWT,
author = "Yiming Li and Yanyan Shen and Lei Chen and Mingxuan
Yuan",
title = "{Zebra}: When Temporal Graph Neural Networks Meet
Temporal Personalized {PageRank}",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "16",
number = "6",
pages = "1332--1345",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.14778/3583140.3583150",
ISSN = "2150-8097",
bibdate = "Mon May 1 07:43:11 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
URL = "https://dl.acm.org/doi/10.14778/3583140.3583150",
abstract = "Temporal graph neural networks (T-GNNs) are
state-of-the-art methods for learning representations
over dynamic graphs. Despite the superior performance,
T-GNNs still suffer from high computational complexity
caused by the tedious recursive temporal \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "https://dl.acm.org/loi/pvldb",
}
@Article{Liao:2023:TKE,
author = "Shengbin Liao and Zongkai Yang and Qingzhou Liao and
Zhangxiong zheng",
title = "{TopicLPRank}: a keyphrase extraction method based on
improved {TopicRank}",
journal = j-J-SUPERCOMPUTING,
volume = "79",
number = "8",
pages = "9073--9092",
month = may,
year = "2023",
CODEN = "JOSUED",
DOI = "https://doi.org/10.1007/s11227-022-05022-0",
ISSN = "0920-8542 (print), 1573-0484 (electronic)",
ISSN-L = "0920-8542",
bibdate = "Thu Apr 6 06:16:05 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s11227-022-05022-0",
acknowledgement = ack-nhfb,
ajournal = "J. Supercomputing",
fjournal = "The Journal of Supercomputing",
journal-URL = "http://link.springer.com/journal/11227",
}
@Article{Pan:2023:PPD,
author = "Weifeng Pan and Hua Ming and Dae-Kyoo Kim and Zijiang
Yang",
title = "Pride: Prioritizing Documentation Effort Based on a
{PageRank}-Like Algorithm and Simple Filtering Rules",
journal = j-IEEE-TRANS-SOFTW-ENG,
volume = "49",
number = "3",
pages = "1118--1151",
month = mar,
year = "2023",
CODEN = "IESEDJ",
DOI = "https://doi.org/10.1109/TSE.2022.3171469",
ISSN = "0098-5589 (print), 1939-3520 (electronic)",
ISSN-L = "0098-5589",
bibdate = "Thu Mar 16 07:29:56 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ieeetranssoftweng2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE Transactions on Software Engineering",
journal-URL = "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32",
}
@Article{Wang:2023:ESN,
author = "Hanzhi Wang and Zhewei Wei",
title = "Estimating Single-Node {PageRank} in {$ \tilde
{O}(\min d_t, \sqrt {m}) $} Time",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "16",
number = "11",
pages = "2949--2961",
month = jul,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.14778/3611479.3611500",
ISSN = "2150-8097",
bibdate = "Fri Aug 25 07:25:43 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
URL = "https://dl.acm.org/doi/10.14778/3611479.3611500",
abstract = "PageRank is a famous measure of graph centrality that
has numerous applications in practice. The problem of
computing a single node's PageRank has been the subject
of extensive research over a decade. However, existing
methods still incur large time complexities despite
years of efforts. Even on undirected graphs where
several valuable properties held by PageRank scores,
the problem of locally approximating the PageRank score
of a target node remains a challenging task. Two
commonly adopted techniques, Monte-Carlo based random
walks and backward push, both cost $O(n)$ time in the
worst-case scenario, which hinders existing methods
from achieving a sublinear time complexity like
$O(\sqrt{m})$ on an undirected graph with $n$ nodes and
$m$ edges.\par
In this paper, we focus on the problem of single-node
PageRank computation on undirected graphs. We propose a
novel algorithm, SetPush, for estimating single-node
PageRank specifically on undirected graphs. With
non-trivial analysis, we prove that our SetPush
achieves the $\tilde{O}(\min(d_, \sqrt{m}))$ time
complexity for estimating the target node $t$'s
PageRank with constant relative error and constant
failure probability on undirected graphs. We conduct
comprehensive experiments to demonstrate the
effectiveness of SetPush.",
acknowledgement = ack-nhfb,
ajournal = "",
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "https://dl.acm.org/loi/pvldb",
}
@Article{Wen:2023:APM,
author = "Chun Wen and Qian-Ying Hu and Zhao-Li Shen",
title = "An adaptively preconditioned multi-step matrix
splitting iteration for computing {PageRank}",
journal = j-NUMER-ALGORITHMS,
volume = "92",
number = "2",
pages = "1213--1231",
month = feb,
year = "2023",
CODEN = "NUALEG",
DOI = "https://doi.org/10.1007/s11075-022-01337-4",
ISSN = "1017-1398 (print), 1572-9265 (electronic)",
ISSN-L = "1017-1398",
bibdate = "Mon Jan 30 12:22:10 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s11075-022-01337-4",
acknowledgement = ack-nhfb,
ajournal = "Numer. Algorithms",
fjournal = "Numerical Algorithms",
journal-URL = "http://link.springer.com/journal/11075",
}
@Article{Yan:2023:EFL,
author = "Yue Yan and Shujuan Jiang and Yanmei Zhang and Cheng
Zhang",
title = "An effective fault localization approach based on
{PageRank} and mutation analysis",
journal = j-J-SYST-SOFTW,
volume = "204",
number = "??",
pages = "??--??",
month = oct,
year = "2023",
CODEN = "JSSODM",
DOI = "https://doi.org/10.1016/j.jss.2023.111799",
ISSN = "0164-1212 (print), 1873-1228 (electronic)",
ISSN-L = "0164-1212",
bibdate = "Wed Sep 13 08:20:35 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jsystsoftw2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0164121223001942",
acknowledgement = ack-nhfb,
articleno = "111799",
fjournal = "Journal of Systems and Software",
journal-URL = "http://www.sciencedirect.com/science/journal/01641212",
}
@Article{Zhang:2023:PPA,
author = "Qi Zhang and Rongxia Tang and Zhengan Yao and Zan-Bo
Zhang",
title = "A parallel {PageRank} algorithm for undirected graph",
journal = j-APPL-MATH-COMP,
volume = "459",
number = "??",
pages = "??--??",
day = "15",
month = dec,
year = "2023",
CODEN = "AMHCBQ",
DOI = "https://doi.org/10.1016/j.amc.2023.128276",
ISSN = "0096-3003 (print), 1873-5649 (electronic)",
ISSN-L = "0096-3003",
bibdate = "Sat Aug 26 11:28:51 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0096300323004459",
acknowledgement = ack-nhfb,
articleno = "128276",
fjournal = "Applied Mathematics and Computation",
journal-URL = "http://www.sciencedirect.com/science/journal/00963003",
}
@Article{Chen:2024:MLP,
author = "Yannan Chen and Wen Li and Jingya Chang",
title = "Multi-Linear Pseudo-{PageRank} for Hypergraph
Partitioning",
journal = j-J-SCI-COMPUT,
volume = "99",
number = "1",
pages = "??--??",
month = apr,
year = "2024",
CODEN = "JSCOEB",
DOI = "https://doi.org/10.1007/s10915-024-02460-1",
ISSN = "0885-7474 (print), 1573-7691 (electronic)",
ISSN-L = "0885-7474",
bibdate = "Thu May 9 09:25:11 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s10915-024-02460-1",
acknowledgement = ack-nhfb,
ajournal = "J. Sci. Comput.",
articleno = "7",
fjournal = "Journal of Scientific Computing",
journal-URL = "http://link.springer.com/journal/10915",
}
@Article{Hu:2024:AEM,
author = "Qian-Ying Hu and Xian-Ming Gu and Chun Wen",
title = "Application of an extrapolation method in the
{Hessenberg} algorithm for computing {PageRank}",
journal = j-J-SUPERCOMPUTING,
volume = "80",
number = "15",
pages = "22836--22859",
month = oct,
year = "2024",
CODEN = "JOSUED",
DOI = "https://doi.org/10.1007/s11227-024-06327-y",
ISSN = "0920-8542 (print), 1573-0484 (electronic)",
ISSN-L = "0920-8542",
bibdate = "Tue Aug 13 06:33:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s11227-024-06327-y",
acknowledgement = ack-nhfb,
ajournal = "J. Supercomputing",
fjournal = "The Journal of Supercomputing",
journal-URL = "http://link.springer.com/journal/11227",
}
@Article{Liu:2024:BEA,
author = "Haoyu Liu and Siqiang Luo",
title = "{BIRD}: Efficient Approximation of Bidirectional
Hidden Personalized {PageRank}",
journal = j-PROC-VLDB-ENDOWMENT,
volume = "17",
number = "9",
pages = "2255--2268",
month = may,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.14778/3665844.3665855",
ISSN = "2150-8097",
ISSN-L = "2150-8097",
bibdate = "Wed Aug 7 06:07:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
URL = "https://dl.acm.org/doi/10.14778/3665844.3665855",
abstract = "In bipartite graph analysis, similarity measures play
a pivotal role in various applications. Among existing
metrics, the Bidirectional Hidden Personalized PageRank
(BHPP) stands out for its superior query quality.
However, the computational expense of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "Proc. VLDB Endowment",
fjournal = "Proceedings of the VLDB Endowment",
journal-URL = "https://dl.acm.org/loi/pvldb",
}
@Article{Shen:2024:PWF,
author = "Zhao-Li Shen and Bruno Carpentieri and Chun Wen and
Jian-Jun Wang and Stefano Serra-Capizzano and Shi-Ping
Du",
title = "Preconditioned weighted full orthogonalization method
for solving singular linear systems from {PageRank}
problems",
journal = j-NUM-LIN-ALG-APPL,
volume = "31",
number = "3",
pages = "e2541:1--e2541:??",
month = may,
year = "2024",
CODEN = "NLAAEM",
DOI = "https://doi.org/10.1002/nla.2541",
ISSN = "1070-5325 (print), 1099-1506 (electronic)",
ISSN-L = "1070-5325",
bibdate = "Tue May 28 13:42:15 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
acknowledgement = ack-nhfb,
ajournal = "Numer. Linear Algebra Appl.",
fjournal = "Numerical Linear Algebra with Applications",
journal-URL = "https://onlinelibrary.wiley.com/journal/10991506",
onlinedate = "10 November 2023",
}
@Article{Yang:2024:SHP,
author = "Fei Yang and Huyin Zhang and Shiming Tao and Xiying
Fan",
title = "Simple hierarchical {PageRank} graph neural networks",
journal = j-J-SUPERCOMPUTING,
volume = "80",
number = "4",
pages = "5509--5539",
month = mar,
year = "2024",
CODEN = "JOSUED",
DOI = "https://doi.org/10.1007/s11227-023-05666-6",
ISSN = "0920-8542 (print), 1573-0484 (electronic)",
ISSN-L = "0920-8542",
bibdate = "Thu Feb 15 10:23:15 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "https://link.springer.com/article/10.1007/s11227-023-05666-6",
acknowledgement = ack-nhfb,
ajournal = "J. Supercomputing",
fjournal = "The Journal of Supercomputing",
journal-URL = "http://link.springer.com/journal/11227",
}
@Article{Zhou:2025:MIF,
author = "Sheng-Wei Zhou and Chun Wen and Zhao-Li Shen and Bruno
Carpentieri",
title = "The {MFPIO} iteration and the {FPMPE} method for
multilinear {PageRank} computations",
journal = j-J-COMPUT-APPL-MATH,
volume = "454",
number = "??",
pages = "??--??",
day = "15",
month = jan,
year = "2025",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/j.cam.2024.116192",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Fri Aug 23 08:18:50 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2025.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0377042724004412",
acknowledgement = ack-nhfb,
articleno = "116192",
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
}
%%% ====================================================================
%%% Cross-referenced entries must come last:
@Proceedings{ACM:2001:CPT,
editor = "{ACM}",
booktitle = "{Conference proceedings: the Tenth International World
Wide Web Conference, Hong Kong, May 1--5, 2001}",
title = "{Conference proceedings: the Tenth International World
Wide Web Conference, Hong Kong, May 1--5, 2001}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "xxii + 770",
year = "2001",
ISBN = "1-58113-348-0",
ISBN-13 = "978-1-58113-348-6",
LCCN = "TK5105.888 .I573 2001",
bibdate = "Mon May 10 14:10:25 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
URL = "http://portal.acm.org/toc.cfm?id=511446",
acknowledgement = ack-nhfb,
meetingname = "International WWW Conference (10th: 2001: Hong Kong,
China)",
subject = "World Wide Web; Congresses",
}
@Proceedings{Bahill:2001:IIC,
editor = "Terry Bahill",
booktitle = "{2001 IEEE International Conference on Systems, Man
and Cybernetics: October 7--10, 2001, Tucson Convention
Center, Tucson, Arizona, USA}",
title = "{2001 IEEE International Conference on Systems, Man
and Cybernetics: October 7--10, 2001, Tucson Convention
Center, Tucson, Arizona, USA}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2001",
ISBN = "0-7803-7087-2, 0-7803-7088-0 (microfiche),
0-7803-7089-9 (CD-ROM)",
ISBN-13 = "978-0-7803-7087-6, 978-0-7803-7088-3 (microfiche),
978-0-7803-7089-0 (CD-ROM)",
LCCN = "TA168 .I18 2001",
bibdate = "Thu May 6 13:33:15 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
note = "IEEE catalog number 01CH37236.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=7658",
acknowledgement = ack-nhfb,
meetingname = "IEEE International Conference on Systems, Man, and
Cybernetics (2001: Tucson, Ariz.)",
subject = "Cybernetics; Congresses; Systems engineering;
Human-machine systems",
}
@Proceedings{Croft:2001:PAI,
editor = "W. Bruce Croft and others",
booktitle = "{Proceedings of the 24th Annual International ACM
SIGIR Conference on Research and Development in
Information Retrieval: New Orleans, Louisiana, USA,
September 9--13, 2001}",
title = "{Proceedings of the 24th Annual International ACM
SIGIR Conference on Research and Development in
Information Retrieval: New Orleans, Louisiana, USA,
September 9--13, 2001}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "xvi + 464",
year = "2001",
ISBN = "1-58113-331-6",
ISBN-13 = "978-1-58113-331-8",
LCCN = "QA76.9.D3 I552 2001",
bibdate = "Wed Jun 1 18:28:55 MDT 2011",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "ACM order number 606010. Special issue of the SIGIR
Forum, {\bf 24} (2001).",
acknowledgement = ack-nhfb,
}
@Proceedings{Anonymous:2002:PIW,
editor = "Anonymous",
booktitle = "{Proceedings of the 11th International World Wide Web
Conference: Sheraton Waikiki, Honolulu, Hawaii, 7--11
May 2002. WWW 2002}",
title = "{Proceedings of the 11th International World Wide Web
Conference: Sheraton Waikiki, Honolulu, Hawaii, 7--11
May 2002}. {WWW} 2002",
publisher = "????",
address = "Honolulu, HI, USA",
pages = "????",
year = "2002",
ISBN = "1-880672-20-0",
ISBN-13 = "978-1-880672-20-4",
LCCN = "????",
bibdate = "Thu May 6 11:07:50 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
acknowledgement = ack-nhfb,
}
@Proceedings{WangLing:2002:PTI,
editor = "Tok {Wang Ling} and others",
booktitle = "{Proceedings of the Third International Conference on
Web Information Systems Engineering: Singapore, 12--14
December, 2002}",
title = "{Proceedings of the Third International Conference on
Web Information Systems Engineering: Singapore, 12--14
December, 2002}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "373",
year = "2002",
ISBN = "0-7695-1766-8",
ISBN-13 = "978-0-7695-1766-7",
LCCN = "TA168 .I583 200",
bibdate = "Thu May 6 13:57:37 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number PR01768.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=8419",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Web Information Systems
Engineering (3rd: 2002: Singapore)",
subject = "World Wide Web; Congresses; Internet; Systems
engineering",
}
@Proceedings{Barbara:2003:PTS,
editor = "Daniel Barbar{\'a}",
booktitle = "{Proceedings of the Third SIAM International
Conference on Data Mining: [San Francisco, CA, May
1--3, 2003]}",
title = "{Proceedings of the Third SIAM International
Conference on Data Mining: [San Francisco, CA, May
1--3, 2003]}",
publisher = pub-SIAM,
address = pub-SIAM:adr,
pages = "xiii + 347",
year = "2003",
ISBN = "0-89871-545-8",
ISBN-13 = "978-0-89871-545-3",
LCCN = "QA76.9.D343",
bibdate = "Thu May 6 10:12:12 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
URL = "http://www.gbv.de/dms/bowker/toc/9780898715453;
http://www.zentralblatt-math.org/zmath/en/search/?an=1076.68524",
acknowledgement = ack-nhfb,
}
@Proceedings{Chick:2003:PWS,
editor = "Stephen E. Chick and others",
booktitle = "{Proceedings of the 2003 Winter Simulation Conference:
Fairmont Hotel, New Orleans, LA, USA, December 7--10,
2003}",
title = "{Proceedings of the 2003 Winter Simulation Conference:
Fairmont Hotel, New Orleans, LA, USA, December 7--10,
2003}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "????",
year = "2003",
ISBN = "0-7803-8131-9",
ISBN-13 = "978-0-7803-8131-5",
LCCN = "QA76.5 .56 2003; QA76.9.C65 .W56 2003",
bibdate = "Thu May 6 13:44:36 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "ACM Order Number 578030. IEEE catalog number
03CH37499.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=8912",
acknowledgement = ack-nhfb,
meetingname = "Winter Simulation Conference (2003: New Orleans, LA)",
subject = "digital computer simulation; congresses; simulation
methods",
}
@Proceedings{Helal:2003:SAI,
editor = "Abdelsalam A. Helal and others",
booktitle = "{2003 Symposium on Applications and the Internet:
proceedings: Orlando, Florida, January 27--31, 2003.
SAINT 2003}",
title = "{2003 Symposium on Applications and the Internet:
proceedings: Orlando, Florida, January 27--31, 2003.
SAINT 2003}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xiv + 430",
year = "2003",
ISBN = "0-7695-1872-9",
ISBN-13 = "978-0-7695-1872-5",
LCCN = "TK5105.875.I57 S95 2003",
bibdate = "Thu May 6 13:49:57 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=8426",
acknowledgement = ack-nhfb,
meetingname = "Symposium on Applications and the Internet (3rd: 2003:
Orlando, Fla.)",
subject = "Internet; Congresses; Application software",
}
@Proceedings{Hencsey:2003:PTI,
editor = "Guszt{\'a}v Hencsey and Bebo White",
booktitle = "{Proceedings of the Twelfth International Conference
on World Wide Web: Budapest, Hungary, May 20--24,
2003}",
title = "{Proceedings of the Twelfth International Conference
on World Wide Web: Budapest, Hungary, May 20--24,
2003}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "xx + 752",
year = "2003",
ISBN = "1-58113-680-3",
ISBN-13 = "978-1-58113-680-7",
LCCN = "TK5105.888 .I58 2003",
bibdate = "Thu May 6 10:19:17 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
library.mit.edu:9909/mit01; z3950.gbv.de:20011/gvk",
URL = "http://portal.acm.org/toc.cfm?id=775152",
acknowledgement = ack-nhfb,
meetingname = "International WWW Conference (12th: 2003: Budapest,
Hungary)",
subject = "World Wide Web; congresses; computer networks;
hypertext systems; Internet",
}
@Proceedings{IEEE:2003:IIS,
editor = "{IEEE}",
booktitle = "{12th IEEE International Symposium on High Performance
Distributed Computing: proceedings: 22--24 June, 2003,
Seattle, Washington}",
title = "{12th IEEE International Symposium on High Performance
Distributed Computing: proceedings: 22--24 June, 2003,
Seattle, Washington}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xii + 283",
year = "2003",
ISBN = "0-7695-1965-2",
ISBN-13 = "978-0-7695-1965-4",
LCCN = "QA76.9.D5 I157 2003",
bibdate = "Thu May 6 09:10:03 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number PR01965.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=8591",
acknowledgement = ack-nhfb,
meetingname = "IEEE International Symposium on High Performance
Distributed Computing (12th: 2003: Seattle, Wash.)",
subject = "electronic data processing; distributed processing;
congresses",
}
@Proceedings{Liu:2003:ISW,
editor = "Jiming Liu and others",
booktitle = "{IEEE \slash WIC International Conference on Web
Intelligence, 2003, Halifax, NS, Canada. WI 2003.
Proceedings}",
title = "{IEEE \slash WIC International Conference on Web
Intelligence, 2003. Halifax, NS, Canada. WI 2003.
Proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxi + 730",
year = "2003",
ISBN = "0-7695-1932-6",
ISBN-13 = "978-0-7695-1932-6",
LCCN = "QA75.5 .I345 2003",
bibdate = "Thu May 6 09:07:36 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8792",
acknowledgement = ack-nhfb,
meetingname = "IEEE/WIC International Conference on Web Intelligence
(2003: Halifax, NS)",
remark = "Held jointly with IEEE/WIC International Conference on
Intelligent Agent Technology.",
subject = "electronic data processing; congresses; artificial
intelligence",
}
@Proceedings{Yang:2003:ICP,
editor = "Chu-Sing Yang and P. Sadayappan and others",
booktitle = "{2003 International Conference on Parallel Processing:
proceedings: 6--9 October, 2003, Kaohsiung, Taiwan}",
title = "{2003 International Conference on Parallel Processing:
proceedings: 6--9 October, 2003, Kaohsiung, Taiwan}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xvi + 647",
year = "2003",
ISBN = "0-7695-2017-0",
ISBN-13 = "978-0-7695-2017-9",
LCCN = "QA76.58 .I55 2003; QA76.6 .I548 2003",
bibdate = "Thu May 6 13:54:24 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number PR02017.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=8782",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Parallel Processing (32nd:
2003: Kao-hsiung, Taiwan)",
subject = "parallel processing (electronic computers);
congresses",
}
@Proceedings{Barolli:2004:ICA,
editor = "Leonard Barolli",
booktitle = "{18th International Conference on Advanced Information
Networking and Applications, 2004. AINA 2004, 29--31
March 2004, [Fukuoka, Japan. Proceedings]}",
title = "{18th International Conference on Advanced Information
Networking and Applications, 2004. AINA 2004, 29--31
March 2004, [Fukuoka, Japan. Proceedings]}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2004",
ISBN = "0-7695-2051-0",
ISBN-13 = "978-0-7695-2051-3",
LCCN = "TK5105.5 .I5616 2004",
bibdate = "Thu May 6 10:25:16 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE Computer Society Order Number P2051.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=9028",
acknowledgement = ack-nhfb,
}
@Proceedings{Ghorbani:2004:PAC,
editor = "Ali A. Ghorbani",
booktitle = "{Proceedings of the 2nd Annual Communication Networks
and Services Research Conference, 19--21 May 2004,
Fredericton, New Brunswick}",
title = "{Proceedings of the 2nd Annual Communication Networks
and Services Research Conference, 19--21 May 2004,
Fredericton, New Brunswick}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xi + 364",
year = "2004",
ISBN = "0-7695-2096-0",
ISBN-13 = "978-0-7695-2096-4",
LCCN = "TK5101.A1",
bibdate = "Thu May 6 10:28:50 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=9316;
http://www.gbv.de/dms/bowker/toc/9780769520964",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2004:SWI,
editor = "{IEEE}",
booktitle = "{SAINT 2004 Workshops: 2004 International Symposium on
Applications and the Internet: Workshops: proceedings:
26--30 January, 2004, Tokyo, Japan}",
title = "{SAINT 2004 Workshops: 2004 International Symposium on
Applications and the Internet: Workshops: proceedings:
26--30 January, 2004, Tokyo, Japan}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxiii + 409",
year = "2004",
ISBN = "0-7695-2050-2",
ISBN-13 = "978-0-7695-2050-6",
LCCN = "TK5105.875.I57 S95 2004",
bibdate = "Thu May 6 09:13:55 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number PR02050.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=8957",
acknowledgement = ack-nhfb,
meetingname = "Symposium on Applications and the Internet (4th: 2004:
Tokyo, Japan)",
subject = "Internet; congresses",
}
@Proceedings{Leonardi:2004:AMW,
editor = "S. (Stefano) Leonardi",
booktitle = "{Algorithms and models for the web-graph: third
international workshop, WAW 2004, Rome, Italy, October
16, 2004: proceedings}",
title = "{Algorithms and models for the web-graph: third
international workshop, WAW 2004, Rome, Italy, October
16, 2004: proceedings}",
volume = "3243",
publisher = pub-SV,
address = pub-SV:adr,
pages = "viii + 187",
year = "2004",
ISBN = "3-540-23427-6",
ISBN-13 = "978-3-540-23427-2",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.9.A43 W695 2004",
bibdate = "Thu May 6 12:25:46 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
series = ser-LNCS,
URL = "http://library.ust.hk/cgi/db/springer.scr?0302-9743/3243;
http://www.loc.gov/catdir/enhancements/fy0823/2004113291-d.html",
acknowledgement = ack-nhfb,
meetingname = "Workshop on Algorithms and Models for the Web-Graph
(3rd: 2004: Rome, Italy)",
remark = "This volume contains papers presented at the 3rd
Workshop on Algorithms and Models for the Web-Graph
(WAW 2004) in conjunction with the 45th Annual IEEE
Symposium on Foundations of Computer Science (FOCS
2004).",
subject = "computer algorithms; congresses; data mining",
}
@Proceedings{Zhong:2004:IWS,
editor = "Ning Zhong and others",
booktitle = "{IEEE\slash WIC \slash ACM International Conference on
Web Intelligence (WI 2004): Beijing, China, September
20--24, 2004: proceedings}",
title = "{IEEE\slash WIC \slash ACM International Conference on
Web Intelligence (WI 2004): Beijing, China, September
20--24, 2004: proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxviii + 811",
year = "2004",
ISBN = "0-7695-2100-2",
ISBN-13 = "978-0-7695-2100-8",
LCCN = "QA75.5 .I345 2004 .I429 2004; Q334 .I429 2004",
bibdate = "Thu May 6 14:08:43 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2100.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=9689",
acknowledgement = ack-nhfb,
meetingname = "IEEE/WIC/ACM International Conference on Web
Intelligence (2004: Beijing, China)",
subject = "Artificial intelligence; Congresses",
}
@Proceedings{Barolli:2005:ICP,
editor = "Leonard Barolli and Jianhua Ma and Laurence Tianruo
Yang",
booktitle = "{11th International Conference on Parallel and
Distributed Systems, July 20--22, 2005, Fukuoka
Institute of Technology (FIT), Fukuoka, Japan:
proceedings}",
title = "{11th International Conference on Parallel and
Distributed Systems, July 20--22, 2005, Fukuoka
Institute of Technology (FIT), Fukuoka, Japan:
proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2005",
ISBN = "0-7695-2281-5",
ISBN-13 = "978-0-7695-2281-4",
LCCN = "QA76.58 .I576 2005",
bibdate = "Thu May 6 09:16:01 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2281.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10248",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Parallel and Distributed
Systems (11th: 2005: Fukuoka-shi, Japan)",
subject = "electronic data processing; distributed processing;
congresses; parallel processing (electronic
computers)",
}
@Proceedings{Han:2005:FII,
editor = "Jiawei Han and others",
booktitle = "{Fifth IEEE International Conference on Data Mining.
ICDM 2005. 27--30 November 2005, Houston, Texas.
Proceedings}",
title = "{Fifth IEEE International Conference on Data Mining.
ICDM 2005. 27--30 November 2005, Houston, Texas.
Proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxvii + 846",
year = "2005",
ISBN = "0-7695-2278-5",
ISBN-13 = "978-0-7695-2278-4",
ISSN = "1550-4786",
LCCN = "QA76.9.D343 I133 2005",
bibdate = "Thu May 6 15:15:08 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE Computer Society Order Number P2278.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10470",
acknowledgement = ack-nhfb,
}
@Proceedings{He:2005:TIC,
editor = "Xiangjian He and others",
booktitle = "{Third International Conference on Information
Technology and Applications (ICITA 2005): 4--7 July
2005, Sydney, Australia: proceedings}",
title = "{Third International Conference on Information
Technology and Applications (ICITA 2005): 4--7 July
2005, Sydney, Australia: proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2005",
ISBN = "0-7695-2316-1",
ISBN-13 = "978-0-7695-2316-3",
LCCN = "T58.5 .I545 2005",
bibdate = "Thu May 6 09:17:57 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society Order Number P2316.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=9966",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Information Technology and
Applications (3rd: 2005: Sydney, NSW)",
subject = "information technology; congresses; application
software",
}
@Proceedings{IEEE:2005:EIC,
editor = "{IEEE}",
booktitle = "{14th Euromicro International Conference on Parallel,
Distributed, and Network-Based Processing: proceedings:
15--17 February 2006: Montb{\'e}liard-Sochaux,
France}",
title = "{14th Euromicro International Conference on Parallel,
Distributed, and Network-Based Processing: proceedings:
15--17 February 2006: Montb{\'e}liard-Sochaux,
France}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xviii + 474",
year = "2005",
ISBN = "0-7695-2513-X",
ISBN-13 = "978-0-7695-2513-6",
LCCN = "QA76.58 .E95 2006",
bibdate = "Thu May 6 10:35:20 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
note = "IEEE Computer Society order number P2513.",
acknowledgement = ack-nhfb,
meetingname = "Euromicro Conference on Parallel, Distributed, and
Network-based Processing (14th: 2006:
Montb\'eliard-Sochaux, France)",
subject = "parallel programming (computer science); congresses;
electronic data processing; distributed processing",
}
@Proceedings{IEEE:2005:ICD,
editor = "{IEEE}",
booktitle = "{25th International Conference on Distributed
Computing Systems: proceedings: 6--10 June, 2005,
Columbus, Ohio, USA}",
title = "{25th International Conference on Distributed
Computing Systems: proceedings: 6--10 June, 2005,
Columbus, Ohio, USA}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xviii + 827",
year = "2005",
ISBN = "0-7695-2331-5",
ISBN-13 = "978-0-7695-2331-6",
LCCN = "QA76.9.D5 I57 2005",
bibdate = "Fri May 7 22:34:07 MDT 2010",
bibsource = "alpha.lib.uwo.ca:210/INNOPAC;
fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
note = "IEEE Computer Society order number P2331.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=9816",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Distributed Computing
Systems (25th: 2005: Columbus, Ohio)",
subject = "electronic data processing; distributed processing;
congresses; computer networks",
}
@Proceedings{IEEE:2005:PII,
editor = "{IEEE}",
booktitle = "{Proceedings of 2005 IEEE International Conference on
Natural Language Processing and Knowledge Engineering:
(IEEE NLP-KE'05): October 30--November 1, 2005, Wuhan,
China}",
title = "{Proceedings of 2005 IEEE International Conference on
Natural Language Processing and Knowledge Engineering:
(IEEE NLP-KE'05): October 30--November 1, 2005, Wuhan,
China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "10 + 843 + 11",
year = "2005",
ISBN = "0-7803-9361-9",
ISBN-13 = "978-0-7803-9361-5",
LCCN = "QA76.9.N38 I563 2005",
bibdate = "Fri May 7 19:25:20 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
note = "IEEE Catalog Number: 05EX1156.",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Natural Language
Processing and Knowledge Engineering (2005: Wuhan,
China)",
}
@Proceedings{Meng:2005:IIC,
editor = "Max Meng and others",
booktitle = "{IEEE International Conference on Information
Acquisition, 2005. 27 June--3 July 2005, [the Chinese
University of Hong Kong and the University of Macau]}",
title = "{IEEE International Conference on Information
Acquisition, 2005. 27 June--3 July 2005, [the Chinese
University of Hong Kong and the University of Macau]}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "609",
year = "2005",
ISBN = "0-7803-9303-1",
ISBN-13 = "978-0-7803-9303-5",
LCCN = "TA165 .I57 2005",
bibdate = "Thu May 6 15:36:25 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE catalog number 05EX1134.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10894",
acknowledgement = ack-nhfb,
}
@Proceedings{Nesi:2005:FIC,
editor = "Paolo Nesi and Kia Ng and Jaime Delgado and others",
booktitle = "{First International Conference on Automated
Production of Cross Media Content for Multi-channel
Distribution: proceedings: Florence, Italy, 30
November--2 December 2005}",
title = "{First International Conference on Automated
Production of Cross Media Content for Multi-channel
Distribution: proceedings: Florence, Italy, 30
November--2 December 2005}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xi + 304",
year = "2005",
ISBN = "0-7695-2348-X",
ISBN-13 = "978-0-7695-2348-4",
LCCN = "QA76.575 .I633 2005",
bibdate = "Thu May 6 15:56:58 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number: P2348.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10605",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Automated Production of
Cross Media Content for Multi-channel Distribution
(1st: 2005: Florence, Italy)",
subject = "multimedia systems; congresses; music; data
processing",
}
@Proceedings{Skowron:2005:PIW,
editor = "Andrzej Skowron",
booktitle = "{Proceedings of the 2005 IEEE\slash WIC\slash ACM
International Conference on Web Intelligence, 2005.
September 19--22, 2005, Compi{\`e}gne University of
Technology, France}",
title = "{Proceedings of the 2005 IEEE\slash WIC\slash ACM
International Conference on Web Intelligence, 2005.
September 19--22, 2005, Compi{\`e}gne University of
Technology, France}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxxii + 819",
year = "2005",
ISBN = "0-7695-2415-X",
ISBN-13 = "978-0-7695-2415-3",
LCCN = "TK5105.888 .I37 2005",
bibdate = "Thu May 6 16:19:56 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE Computer Society Order Number P2415.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10179;
http://www.gbv.de/dms/bowker/toc/9780769524153",
acknowledgement = ack-nhfb,
}
@Proceedings{Barga:2006:IPI,
editor = "Roger S. Barga and Xiaofang Zhou",
booktitle = "{ICDE '06: proceedings: 22nd International Conference
on Data Engineering workshops: 3--7 April, 2006,
Atlanta, Georgia}",
title = "{ICDE '06: proceedings: 22nd International Conference
on Data Engineering workshops: 3--7 April, 2006,
Atlanta, Georgia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2006",
ISBN = "0-7695-2571-7",
ISBN-13 = "978-0-7695-2571-6",
LCCN = "QA76.9.D3 I5582 2006",
bibdate = "Thu May 6 14:28:27 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10810",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Data Engineering (22nd:
2006: Atlanta, Ga.). Workshops",
subject = "database management; congresses; electronic data
processing",
}
@Proceedings{Clifton:2006:SIC,
editor = "Christopher Wade Clifton and others",
booktitle = "{Sixth International Conference on Data Mining: ICDM
2006: proceedings: 18--22 December, 2006, Hong Kong}",
title = "{Sixth International Conference on Data Mining: ICDM
2006: proceedings: 18--22 December, 2006, Hong Kong}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxviii + 1221",
year = "2006",
ISBN = "0-7695-2702-7",
ISBN-13 = "978-0-7695-2702-4",
LCCN = "QA76.9.D343 I133 2006",
bibdate = "Thu May 6 15:43:32 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2701.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4053012",
acknowledgement = ack-nhfb,
meetingname = "ICDM Workshops (2006: Hong Kong, China)",
subject = "data mining; congresses",
}
@Proceedings{Feng:2006:IMM,
editor = "Huamin Feng and Shiqiang Yang and Yueting Zhuang",
booktitle = "{The 12th International MuIti-Media ModelIing
Conference proceedings: MMM2006, 4--6 January 2006,
Beijing, China}",
title = "{The 12th International MuIti-Media ModelIing
Conference proceedings: MMM2006, 4--6 January 2006,
Beijing, China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "489 + 3",
year = "2006",
ISBN = "1-4244-0028-7",
ISBN-13 = "978-1-4244-0028-7",
LCCN = "QA76.575 .I6526 2006",
bibdate = "Thu May 6 14:44:51 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE catalog number: 06EX1249.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10988",
acknowledgement = ack-nhfb,
meetingname = "International Multimedia Modelling Conference (12th:
2006: Beijing, China)",
subject = "Multimedia systems; Congresses; Computer graphics",
}
@Proceedings{IEEE:2006:AAC,
editor = "{IEEE}",
booktitle = "{ADCOM 2006: autonomic computing: proceedings: 2006
(14th) International Conference on Advanced Computing
and Communications: December 20--23, 2006, National
Institute of Technology Karnataka, Surathkal, India}",
title = "{ADCOM 2006: autonomic computing: proceedings: 2006
(14th) International Conference on Advanced Computing
and Communications: December 20--23, 2006, National
Institute of Technology Karnataka, Surathkal, India}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
year = "2006",
ISBN = "1-4244-0716-8",
ISBN-13 = "978-1-4244-0716-3",
LCCN = "QA75.5 .I5745 2006eb",
bibdate = "Thu May 6 17:36:18 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.lib.umich.edu:210/miu01_pub",
note = "IEEE catalog number 06EX1537C.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4289832",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Advanced Computing and
Communications (14th: 2006: Karnataka, India)",
subject = "computers; congresses; computer systems; electronic
data processing; autonomic computing",
}
@Proceedings{IEEE:2006:AIS,
editor = "{IEEE}",
booktitle = "{47th Annual IEEE Symposium on Foundations of Computer
Science: FOCS 2006: 21--24 October, 2006, Berkeley,
California}",
title = "{47th Annual IEEE Symposium on Foundations of Computer
Science: FOCS 2006: 21--24 October, 2006, Berkeley,
California}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xiv + 750",
year = "2006",
ISBN = "0-7695-2720-5, 0-7695-2362-5",
ISBN-13 = "978-0-7695-2720-8, 978-0-7695-2362-0",
LCCN = "QA76 .S974 2006",
bibdate = "Thu May 6 08:30:22 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2720.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4031329",
acknowledgement = ack-nhfb,
meetingname = "Symposium on Foundations of Computer Science (47th:
2006: Berkeley, California)",
subject = "electronic data processing; congresses; machine
theory",
}
@Proceedings{IEEE:2006:ASE,
editor = "{IEEE}",
booktitle = "{2006 Australian Software Engineering Conference:
ASWEC 2006: 18--21 April, 2006, Sydney, Australia}",
title = "{2006 Australian Software Engineering Conference:
ASWEC 2006: 18--21 April, 2006, Sydney, Australia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xv + 422",
year = "2006",
ISBN = "0-7695-2551-2",
ISBN-13 = "978-0-7695-2551-8",
LCCN = "QA76.758 .A89 2006",
bibdate = "Thu May 6 15:15:52 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2251.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10753",
acknowledgement = ack-nhfb,
meetingname = "Australian Software Engineering Conference (2006:
Sydney, NSW)",
subject = "Software engineering; Congresses",
}
@Proceedings{IEEE:2006:CIT,
editor = "{IEEE}",
booktitle = "{Communications and Information Technologies, 2006.
ISCIT '06, International Symposium on: Oct. 18
2006--Sept. 20 2006, [Bangkok, Thailand]}",
title = "{Communications and Information Technologies, 2006.
ISCIT '06, International Symposium on: Oct. 18
2006--Sept. 20 2006, [Bangkok, Thailand]}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "936",
year = "2006",
ISBN = "0-7803-9741-X",
ISBN-13 = "978-0-7803-9741-5",
LCCN = "TK5105; TK5105eb; Internet",
bibdate = "Thu May 6 14:38:56 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4141327",
acknowledgement = ack-nhfb,
meetingname = "International Symposium on Communications and
Information Technologies (6th: 2006: Bangkok,
Thailand)",
subject = "data transmission systems; congresses; information
technology; Internet; signal processing",
}
@Proceedings{IEEE:2006:ICC,
editor = "{IEEE}",
booktitle = "{9th International Conference on Control, Automation,
Robotics and Vision, 2006. ICARCV '06. 5--8 December
2006, Singapore}",
title = "{9th International Conference on Control, Automation,
Robotics and Vision, 2006. ICARCV '06. 5--8 December
2006, Singapore}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxii + 2428",
year = "2006",
ISBN = "1-4244-0341-3",
ISBN-13 = "978-1-4244-0341-7",
LCCN = "TJ212.2 .I5474 2006",
bibdate = "Thu May 6 15:06:01 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE catalog number 06EX1361.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4149990",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2006:IIM,
editor = "{IEEE}",
booktitle = "{ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne,
Mauritius, 23--29 April, 2006}",
title = "{ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne,
Mauritius, 23--29 April, 2006}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
year = "2006",
ISBN = "0-7695-2552-0",
ISBN-13 = "978-0-7695-2552-5",
LCCN = "See",
bibdate = "Thu May 6 17:38:14 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.lib.umich.edu:210/miu01_pub",
note = "IEEE Computer Society order number P2552.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10841",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Networking (5th: 2006:
Morne, Mauritius)",
subject = "computer networks; congresses; computer systems;
mobile communication systems in education",
}
@Proceedings{IEEE:2006:IJC,
editor = "{IEEE}",
booktitle = "{2006 International Joint Conference on Neural
Networks, Sheraton Vancouver Wall Centre Hotel,
Vancouver, BC, Canada, July 16--21, 2006}",
title = "{2006 International Joint Conference on Neural
Networks, Sheraton Vancouver Wall Centre Hotel,
Vancouver, BC, Canada, July 16--21, 2006}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
year = "2006",
ISBN = "0-7803-9490-9",
ISBN-13 = "978-0-7803-9490-2",
LCCN = "QA76.87 2006",
bibdate = "Thu May 6 16:16:00 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11216",
acknowledgement = ack-nhfb,
meetingname = "International Joint Conference on Neural Networks
(2006: Vancouver, BC)",
}
@Proceedings{IEEE:2006:ISL,
editor = "{IEEE}",
booktitle = "{2006 IEEE Spoken Language Technology Workshop: Palm
Beach, Aruba, 10--13 December 2006}",
title = "{2006 IEEE Spoken Language Technology Workshop: Palm
Beach, Aruba, 10--13 December 2006}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xiii + 261 + 2",
year = "2006",
ISBN = "1-4244-0872-5 (softbound edition)",
ISBN-13 = "978-1-4244-0872-6 (softbound edition)",
LCCN = "TK7895.S65",
bibdate = "Thu May 6 16:42:21 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE catalog number 06EX1646.",
acknowledgement = ack-nhfb,
subject = "automatic speech recognition; congresses; natural
language processing (computer science); computational
linguistics",
}
@Proceedings{IEEE:2006:ISP,
editor = "{IEEE}",
booktitle = "{International Symposium on Parallel Computing in
Electrical Engineering, 2006. PARELEC 2006. 13--17
September 2006, Bialystok, Poland. Proceedings}",
title = "{International Symposium on Parallel Computing in
Electrical Engineering, 2006. PARELEC 2006. 13--17
September 2006, Bialystok, Poland. Proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xvii + 478",
year = "2006",
ISBN = "0-7695-2554-7",
ISBN-13 = "978-0-7695-2554-9",
LCCN = "QA76.58. I578 2006",
bibdate = "Thu May 6 15:57:44 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE Computer Society order number P2554.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11156",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2006:SIC,
editor = "{IEEE}",
booktitle = "{Sixth International Conference on Advanced Learning
Technologies, 2006. ICALT 2006. 5--7 July 2006,
Kerkrade, The Netherlands. Proceedings}",
title = "{Sixth International Conference on Advanced Learning
Technologies, 2006. ICALT 2006 5--7 July 2006,
Kerkrade, The Netherlands. Proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxxi + 1215",
year = "2006",
ISBN = "1-4244-3075-5",
ISBN-13 = "978-1-4244-3075-8",
LCCN = "????",
bibdate = "Thu May 6 16:06:29 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10997",
acknowledgement = ack-nhfb,
remark = "Parallel als Druckausg. erschienen.",
}
@Proceedings{IEEE:2006:WSW,
editor = "{IEEE}",
booktitle = "{WCICA 2006: Six World Congress on Intelligent Control
and Automation: June 21--23, 2006, Dalian, China}",
title = "{WCICA 2006: Six World Congress on Intelligent Control
and Automation: June 21--23, 2006, Dalian, China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
year = "2006",
ISBN = "1-4244-0332-4",
ISBN-13 = "978-1-4244-0332-5",
LCCN = "TJ217.5 2006",
bibdate = "Thu May 6 09:24:59 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE catalog number 06EX1358C.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11210",
acknowledgement = ack-nhfb,
meetingname = "World Congress on Intelligent Control and Automation
(6th: 2006: Dalian Shi, China)",
subject = "intelligent control systems; congresses; automation",
}
@Proceedings{Jeong:2006:SII,
editor = "Chang-Sung Jeong and others",
booktitle = "{Sixth IEEE International Conference on Computer and
Information Technology: CIT 2006. 20--22 September
2006, Korea University, Seoul, Korea}",
title = "{Sixth IEEE International Conference on Computer and
Information Technology: CIT 2006. 20--22 September
2006, Korea University, Seoul, Korea}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxiv + 276",
year = "2006",
ISBN = "0-7695-2687-X",
ISBN-13 = "978-0-7695-2687-4",
LCCN = "T58.5 .I5662 2006",
bibdate = "Thu May 6 16:16:17 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE Computer Society order number E2687.",
URL = "http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=4019822",
acknowledgement = ack-nhfb,
}
@Proceedings{Nishida:2006:IWA,
editor = "T. (Toyoaki) Nishida and others",
booktitle = "{2006 IEEE\slash WIC\slash ACM International
Conference on Web Intelligence: (WI 2006 main
conference proceedings) (WI '06): proceedings: 18--22
December 2006, Hong Kong, China}",
title = "{2006 IEEE\slash WIC\slash ACM International
Conference on Web Intelligence: (WI 2006 main
conference proceedings) (WI '06): proceedings: 18--22
December 2006, Hong Kong, China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxviii + 1085",
year = "2006",
ISBN = "0-7695-2747-7",
ISBN-13 = "978-0-7695-2747-5",
LCCN = "TK5105.888 2006",
bibdate = "Thu May 6 16:02:18 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
library.mit.edu:9909/mit01",
note = "IEEE Computer Society order number P2747.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4061321",
acknowledgement = ack-nhfb,
meetingname = "IEEE\slash WIC\slash ACM International Conference on
Web Intelligence (6th: 2006: Hong Kong, China)",
subject = "World Wide Web; congresses; artificial intelligence;
data mining",
}
@Proceedings{Perner:2006:ADM,
editor = "Petra Perner",
booktitle = "{Advances in data mining: applications in medicine,
web mining, marketing, image and signal mining: 6th
Industrial Conference on Data Mining, ICDM 2006,
Leipzig, Germany, July 14--15, 2006: proceedings}",
title = "{Advances in data mining: applications in medicine,
web mining, marketing, image and signal mining: 6th
Industrial Conference on Data Mining, ICDM 2006,
Leipzig, Germany, July 14--15, 2006: proceedings}",
volume = "4065",
publisher = pub-SV,
address = pub-SV:adr,
pages = "xi + 592",
year = "2006",
ISBN = "3-540-36036-0",
ISBN-13 = "978-3-540-36036-0",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.9.D343",
bibdate = "Thu May 06 17:11:23 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
prodorbis.library.yale.edu:7090/voyager",
series = ser-LNCS,
acknowledgement = ack-nhfb,
meetingname = "Industrial Conference on Data Mining (6th: 2006:
Leipzig, Germany)",
subject = "Data mining; Congresses",
}
@Proceedings{Turner:2006:SII,
editor = "Stephen John Turner and Bu Sung Lee and Wientong Cai",
booktitle = "{Sixth IEEE International Symposium on Cluster
Computing and the Grid workshops, 2006: CCGrid 06.
16--19 May 2006, Singapore}",
title = "{Sixth IEEE International Symposium on Cluster
Computing and the Grid workshops, 2006: CCGrid 06.
16--19 May 2006, Singapore}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxiii + 662",
year = "2006",
ISBN = "0-7695-2585-7",
ISBN-13 = "978-0-7695-2585-3",
LCCN = "QA76.9.C58 I133 2006",
bibdate = "Thu May 6 14:57:13 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE Computer Society Order Number P2585.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10857",
acknowledgement = ack-nhfb,
}
@Proceedings{Wombacher:2006:JCC,
editor = "Andreas Wombacher and Christian Huemer and Markus
Stolze and others",
booktitle = "{Joint Conference: CEC\slash EEE 2006 Joint
Conference: 8th IEEE International Conference on
E-Commerce and Technology (CEC 2006): 3rd IEEE
International Conference on Enterprise Computing,
E-Commerce and E-Services (EEE 2006): 3rd IEEE
International Workshop on Mobile Commerce and Wireless
Services (WMCS 2006): Joint Workshop: 2nd International
Workshop on Business Service Networks (BSN 2006): 2nd
International Workshop on Service Oriented Solutions
for Cooperative Organizations (SoS4CO ): June 26--29,
2006, San Francisco, California}",
title = "{Joint Conference: CEC\slash EEE 2006 Joint
Conference: 8th IEEE International Conference on
E-Commerce and Technology (CEC 2006): 3rd IEEE
International Conference on Enterprise Computing,
E-Commerce and E-Services (EEE 2006): 3rd IEEE
International Workshop on Mobile Commerce and Wireless
Services (WMCS 2006): Joint Workshop: 2nd International
Workshop on Business Service Networks (BSN 2006): 2nd
International Workshop on Service Oriented Solutions
for Cooperative Organizations (SoS4CO ): June 26--29,
2006, San Francisco, California}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxiv + 588",
year = "2006",
ISBN = "0-7695-2511-3",
ISBN-13 = "978-0-7695-2511-2",
LCCN = "HF5548.32 .I57 2006",
bibdate = "Thu May 6 15:53:12 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10920",
acknowledgement = ack-nhfb,
meetingname = "IEEE International Conference on E-commerce
Technology. Workshops and Conferences (8th: 2006: San
Francisco, Calif.)",
subject = "electronic commerce; congresses; organizational
change",
}
@Proceedings{Zhang:2006:IIC,
editor = "Yan-Qing Zhang and Tsau Y. Lin",
booktitle = "{2006 IEEE International Conference on Granular
Computing: Atlanta, USA, May 10--12, 2006}",
title = "{2006 IEEE International Conference on Granular
Computing: Atlanta, USA, May 10--12, 2006}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
year = "2006",
ISBN = "1-4244-0134-8",
ISBN-13 = "978-1-4244-0134-5",
LCCN = "QA76.9.S63 2006",
bibdate = "Thu May 6 09:54:29 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE catalog number 06EX1286.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10898",
acknowledgement = ack-nhfb,
meetingname = "IEEE International Conference on Granular Computing
(2006: Atlanta, Ga.)",
subject = "granular computing; congresses",
}
@Proceedings{Bonato:2007:AMW,
editor = "Anthony Bonato and Fan R. K. Chung",
booktitle = "{Algorithms and models for the web-graph: 5th
international workshop, WAW 2007, San Diego, CA, USA,
December 11-12, 2007: proceedings}",
title = "{Algorithms and models for the web-graph: 5th
international workshop, WAW 2007, San Diego, CA, USA,
December 11-12, 2007: proceedings}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "x + 216",
year = "2007",
ISBN = "3-540-77003-8 (softcover)",
ISBN-13 = "978-3-540-77003-9 (softcover)",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.9.A43 W39 2007; QA76.9.A43 W428 2007; QA76.9.A43
W428 2007; Internet; QA76.9.A43 W63 2007",
bibdate = "Thu May 6 08:22:51 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
series = ser-LNCS,
acknowledgement = ack-nhfb,
meetingname = "WAW 2007 (2007: San Diego, Calif.)",
subject = "computer algorithms; congresses; data mining",
}
@Proceedings{Cai:2007:TAM,
editor = "Jin-yi Cai and S. B. (S. Barry) Cooper and Hong Zhu",
booktitle = "{Theory and applications of models of computation: 4th
international conference, TAMC 2007, Shanghai, China,
May 22--25, 2007: proceedings}",
title = "{Theory and applications of models of computation: 4th
international conference, TAMC 2007, Shanghai, China,
May 22--25, 2007: proceedings}",
volume = "4484",
publisher = pub-SV,
address = pub-SV:adr,
pages = "xiii + 772",
year = "2007",
ISBN = "3-540-72503-2 (softcover)",
ISBN-13 = "978-3-540-72503-9 (softcover)",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA267.7 .T36 2007",
bibdate = "Thu May 6 10:38:25 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
series = ser-LNCS,
URL = "http://www.springerlink.com/openurl.asp?genre=issue&issn=0302-9743&volume=4484",
acknowledgement = ack-nhfb,
meetingname = "TAMC 2007 (2007: Shanghai, China)",
subject = "Computational complexity; Congresses; Computable
functions",
}
@Proceedings{Dini:2007:SIC,
editor = "Oana Dini and others",
booktitle = "{Second International Conference on Systems and
Networks Communications: ICSNC 2007, 25--31 August
2007. HPC-Bio 2007: the First International Workshop on
High Performance Computing Applied to Medical Data and
Bioinformatics}",
title = "{Second International Conference on Systems and
Networks Communications: ICSNC 2007, 25--31 August
2007. HPC-Bio 2007: the First International Workshop on
High Performance Computing Applied to Medical Data and
Bioinformatics}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2007",
ISBN = "0-7695-2938-0",
ISBN-13 = "978-0-7695-2938-7",
LCCN = "TK5105.5 I5727 2006e",
bibdate = "Thu May 6 15:27:18 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2938.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4299965",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Systems and Networks
Communications (2nd: 2007: Riviera, France)",
subject = "computer networks; congresses; wireless communication
systems",
}
@Proceedings{Feng:2007:EAI,
editor = "Wenying Feng and Feng Gao",
booktitle = "{Eighth ACIS International Conference on Software
Engineering, Artificial Intelligence, Networking, and
Parallel/Distributed Computing: SNPD 2007: [in
conjunction with 3rd [i.e. 8th] ACIS International
Workshop on Self-Assembling Networks: SAWN 2007]:
proceedings: 30 July--1 August 2007, Haier
International Training Center, Qingdao, China}",
title = "{Eighth ACIS International Conference on Software
Engineering, Artificial Intelligence, Networking, and
Parallel/Distributed Computing: SNPD 2007: [in
conjunction with 3rd [i.e. 8th] ACIS International
Workshop on Self-Assembling Networks: SAWN 2007]:
proceedings: 30 July--1 August 2007, Haier
International Training Center, Qingdao, China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2007",
ISBN = "0-7695-2909-7",
ISBN-13 = "978-0-7695-2909-7",
LCCN = "QA76.758 .I573155",
bibdate = "Thu May 6 09:32:55 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2909.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4287452",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Software Engineering,
Artificial Intelligence, Networking, and
Parallel/Distributed Computing (8th: 2007: Qingdao Shi,
China)",
subject = "Software engineering; Congresses; Artificial
intelligence; Wireless communication systems",
}
@Proceedings{Hauswirth:2007:SII,
editor = "Manfred Hauswirth and others",
booktitle = "{Seventh IEEE International Conference on Peer-to-Peer
Computing, 2007. P2P 2007. 2--5 Sept. 2007, Galway,
Ireland. Proceedings}",
title = "{Seventh IEEE International Conference on Peer-to-Peer
Computing, 2007. P2P 2007. 2--5 Sept. 2007, Galway,
Ireland. Proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xiii + 253",
year = "2007",
ISBN = "0-7695-2986-0",
ISBN-13 = "978-0-7695-2986-8",
LCCN = "TK5105.525 .I58 2007",
bibdate = "Thu May 6 17:07:24 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE catalog nunber PR2986.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4343447",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2007:BBE,
editor = "{IEEE}",
booktitle = "{Bioinformatics and Biomedical Engineering, 2007:
ICBBE 2007: The 1st International Conference, 6--8 July
2007, Wuhan, China}",
title = "{Bioinformatics and Biomedical Engineering, 2007:
ICBBE 2007: The 1st International Conference, 6--8 July
2007, Wuhan, China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2007",
ISBN = "1-4244-1120-3",
ISBN-13 = "978-1-4244-1120-7",
LCCN = "QH324.2 2007",
bibdate = "Thu May 6 16:53:30 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE catalog number 07EX1744.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4272484",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Bioinformatics and
Biomedical Engineering (1st: 2007: Wuhan, China)",
subject = "bioinformatics; congresses; biomedical engineering",
}
@Proceedings{IEEE:2007:ICA,
editor = "{IEEE}",
booktitle = "{21st International Conference on Advanced Networking
and Applications Workshops/Symposia: proceedings:
Niagara Falls, Ontario, Canada: 21--23 May, 2007. AINA
'07}",
title = "{21st International Conference on Advanced Networking
and Applications Workshops/Symposia: proceedings:
Niagara Falls, Ontario, Canada: 21--23 May, 2007. AINA
'07}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2007",
ISBN = "0-7695-2847-3",
ISBN-13 = "978-0-7695-2847-2",
LCCN = "TK5105.5 2007; TK5105.5 .I5616 2007",
bibdate = "Thu May 6 09:31:02 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2847.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4221005",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Advanced Information
Networking and Applications (21st: 2007: Niagara Falls,
Ont.)",
subject = "Computer networks; Congresses; Information networks",
}
@Proceedings{IEEE:2007:ICC,
editor = "{IEEE}",
booktitle = "{2nd International Conference on Communication Systems
Software and Middleware, 2007. COMSWARE 2007. 7--12
January 2007, Bangalore, India}",
title = "{2nd International Conference on Communication Systems
Software and Middleware, 2007. COMSWARE 2007. 7--12
January 2007, Bangalore, India}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxvi + 994",
year = "2007",
ISBN = "1-4244-0614-5, 1-4244-0613-7",
ISBN-13 = "978-1-4244-0614-2, 978-1-4244-0613-5",
LCCN = "TK5101.A1 I479696 2007",
bibdate = "Thu May 6 16:48:08 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE catalog number 07EX1518.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4267954",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2007:ICI,
editor = "{IEEE}",
booktitle = "{IPDPS 2007 California: International Parallel and
Distributed Processing Symposium: proceedings: 21st
International Parallel and Distributed Processing
Symposium: March 26--30, 2007, Long Beach, California,
USA}",
title = "{IPDPS 2007 California: International Parallel and
Distributed Processing Symposium: proceedings: 21st
International Parallel and Distributed Processing
Symposium: March 26--30, 2007, Long Beach, California,
USA}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxxv + 381",
year = "2007",
ISBN = "1-4244-0909-8 (paperback), 1-4244-0910-1 (CD-ROM)",
ISBN-13 = "978-1-4244-0909-9 (paperback), 978-1-4244-0910-5
(CD-ROM)",
LCCN = "QA76.58 .I586 2007",
bibdate = "Thu May 6 15:14:44 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
note = "IEEE catalog number 07TH8938.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4203121",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2007:ICN,
editor = "{IEEE}",
booktitle = "{International Conference on Natural Language
Processing and Knowledge Engineering, 2007. NLP-KE
2007. August 30--September 1, 2007, Beijing, China}",
title = "{International Conference on Natural Language
Processing and Knowledge Engineering, 2007. NLP-KE
2007. August 30--September 1, 2007, Beijing, China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
year = "2007",
ISBN = "1-4244-1611-6",
ISBN-13 = "978-1-4244-1611-0",
LCCN = "????",
bibdate = "Fri May 7 22:00:55 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
odin2.bib.sdu.dk:210/Horizon",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2007:IWM,
editor = "{IEEE}",
booktitle = "{IEEE 9th Workshop on Multimedia Signal Processing,
2007. MMSP 2007. 1--3 October 2007, Chania, Crete,
Greece. Proceedings}",
title = "{IEEE 9th Workshop on Multimedia Signal Processing,
2007. MMSP 2007. 1--3 October 2007, Chania, Crete,
Greece. Proceedings}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xvi + 468",
year = "2007",
ISBN = "1-4244-1273-0",
ISBN-13 = "978-1-4244-1273-0",
LCCN = "????",
bibdate = "Thu May 6 15:02:35 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE catalog number 07EX1807.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4412795",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2007:PTI,
editor = "{IEEE}",
booktitle = "{Proceedings, Third International Conference on
Semantics, Knowledge and Grid: SKG 2007: Xi'an, Shan
Xi, China, 29--31 October 2007}",
title = "{Proceedings, Third International Conference on
Semantics, Knowledge and Grid: SKG 2007: Xi'an, Shan
Xi, China, 29--31 October 2007}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xv + 632",
year = "2007",
ISBN = "0-7695-3007-9",
ISBN-13 = "978-0-7695-3007-9",
LCCN = "TK5105.88815 2007",
bibdate = "Thu May 6 15:40:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P3007.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4438492",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Semantics, Knowledge and
Grid (3rd: 2007: Xi'an Shi, China)",
subject = "semantic networks (information theory); congresses;
knowledge management; computational grids (computer
systems)",
}
@Proceedings{IEEE:2007:SICa,
editor = "{IEEE}",
booktitle = "{2007 Second International Conference on Bio-Inspired
Computing: Theories and Applications: (BIC-TA 2007);
Zhengzhou University of Light Industry, China, 14--17
September 2007}",
title = "{2007 Second International Conference on Bio-Inspired
Computing: Theories and Applications: (BIC-TA 2007);
Zhengzhou University of Light Industry, China, 14--17
September 2007}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "288",
year = "2007",
ISBN = "1-4244-4105-6",
ISBN-13 = "978-1-4244-4105-1",
LCCN = "QA76.87",
bibdate = "Thu May 6 14:48:32 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE catalog number CFP0701F-PRT.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4801442",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2007:SICb,
editor = "{IEEE}",
booktitle = "{Second International Conference on Internet and Web
Applications and Services: ICIW 2007: May 13--19, 2007,
Morne, Maurititus: ENSYS 2007, the Second Workshop on
Entertainment Systems: P2PSA 2007, the Second
International Workshop on P2P Systems and Applications:
ONLINE 2007, the Second International Workshop on
Online Communications, Collaborative Systems, and
Social Network.: Internet and Web Applications and
Services, 2007, ICIW '07, Second International
Conference on}",
title = "{Second International Conference on Internet and Web
Applications and Services: ICIW 2007: May 13--19, 2007,
Morne, Maurititus: ENSYS 2007, the Second Workshop on
Entertainment Systems: P2PSA 2007, the Second
International Workshop on P2P Systems and Applications:
ONLINE 2007, the Second International Workshop on
Online Communications, Collaborative Systems, and
Social Network.: Internet and Web Applications and
Services, 2007, ICIW '07, Second International
Conference on}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "viii + 67",
year = "2007",
ISBN = "0-7695-2844-9",
ISBN-13 = "978-0-7695-2844-1",
LCCN = "TK5105.888 .I563 2007",
bibdate = "Thu May 6 16:29:56 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
library.mit.edu:9909/mit01",
note = "IEEE Computer Society order number E2844.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4222895",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Internet and Web
Applications and Services (2nd: 2007: Le Morne,
Mauritis)",
}
@Proceedings{Lei:2007:FPF,
editor = "Jingsheng Lei and Jian Yu and Shuigeng Zhou",
booktitle = "{FSKD 2007: proceedings: Fourth International
Conference on Fuzzy Systems and Knowledge Discovery:
24--27 August, 2007: Haikou, Hainan, China}",
title = "{FSKD 2007: proceedings: Fourth International
Conference on Fuzzy Systems and Knowledge Discovery:
24--27 August, 2007: Haikou, Hainan, China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2007",
ISBN = "1-4244-1210-2, 0-7695-2874-0",
ISBN-13 = "978-1-4244-1210-5, 978-0-7695-2874-8",
LCCN = "TJ212.2 .I143 2007",
bibdate = "Thu May 6 10:42:54 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
library.mit.edu:9909/mit01",
note = "Four volumes. IEEE Computer Society order number
P2874.",
URL = "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4405869&isYear=2007
(vol. 1);
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4406026&isYear=2007
(vol. 2);
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4406182&isYear=2007
(vol. 3);
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4406334&isYear=2007
(vol. 4)",
acknowledgement = ack-nhfb,
meetingname = "FSKD 2007 (2007: Haikou Shi, China)",
subject = "Fuzzy systems; Congresses; Expert systems (Computer
science)",
}
@Proceedings{Lin:2007:PIW,
editor = "Tsau Y. Lin and others",
booktitle = "{Proceedings of the IEEE\slash WIC\slash ACM
International Conference on Web Intelligence (WI 2007):
November 2--5, 2007, Fremont Marriott Hotel, Silicon
Valley, USA}",
title = "{Proceedings of the IEEE\slash WIC\slash ACM
International Conference on Web Intelligence (WI 2007):
November 2--5, 2007, Fremont Marriott Hotel, Silicon
Valley, USA}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxxiv + 841",
year = "2007",
ISBN = "0-7695-3026-5",
ISBN-13 = "978-0-7695-3026-0",
LCCN = "QA76.76.I58; TK5105.888 .I35687 2007",
bibdate = "Thu May 6 14:36:01 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P3026.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4427043",
acknowledgement = ack-nhfb,
meetingname = "IEEE/WIC/ACM International Conference on Web
Intelligence (2007: Silicon Valley, Calif.)",
subject = "intelligent agents (computer software); congresses;
artificial intelligence",
}
@Proceedings{Luzar-Stiffler:2007:PII,
editor = "Vesna Luzar-Stiffler",
booktitle = "{Proceedings of the ITI 2007, 29th International
Conference on Information Technology Interfaces, June
25--28, 2007, Cavtat\slash Dubrovnik, Croatia}",
title = "{Proceedings of the ITI 2007, 29th International
Conference on Information Technology Interfaces, June
25--28, 2007, Cavtat\slash Dubrovnik, Croatia}",
publisher = "SRCE University Computing Centre, University of
Zagreb",
address = "Zagreb, Croatia",
pages = "????",
year = "2007",
ISBN = "953-7138-10-0",
ISBN-13 = "978-953-7138-10-3",
LCCN = "????",
bibdate = "Thu May 6 15:17:46 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
acknowledgement = ack-nhfb,
remark = "IEEE catalog number 07EX1589C.",
}
@Proceedings{Miyazaki:2007:CPI,
editor = "T. (Toshiaki) Miyazaki and Incheon Paik and Daming Wei
and others",
booktitle = "{CIT 2007: proceedings: 7th IEEE International
Conference on Computer and Information Technology:
16--19 October, 2007, Aizu-Wakamatsu City, Fukushima,
Japan}",
title = "{CIT 2007: proceedings: 7th IEEE International
Conference on Computer and Information Technology:
16--19 October, 2007, Aizu-Wakamatsu City, Fukushima,
Japan}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxvi + 1131",
year = "2007",
ISBN = "0-7695-2983-6",
ISBN-13 = "978-0-7695-2983-7",
LCCN = "T58.5 .I56624 2007",
bibdate = "Thu May 6 15:49:36 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2983.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4385040",
acknowledgement = ack-nhfb,
meetingname = "IEEE International Conference on Computer and
Information Technology (7th: 2007: Aizuwakamatsu-shi,
Japan)",
subject = "information technology; congresses; computers",
}
@Proceedings{Na:2007:IIC,
editor = "Yun Ji Na and others",
booktitle = "{ICCIT 2007: the 2007 International Conference on
Convergence Information Technology: Hydai Hotel,
Gyeongju, Korea, 21--23 November, 2007}",
title = "{ICCIT 2007: the 2007 International Conference on
Convergence Information Technology: Hydai Hotel,
Gyeongju, Korea, 21--23 November, 2007}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2007",
ISBN = "0-7695-3038-9",
ISBN-13 = "978-0-7695-3038-3",
LCCN = "????",
bibdate = "Thu May 6 16:41:46 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
library.mit.edu:9909/mit01",
note = "IEEE Computer Society order number E3038.",
URL = "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4420217&isYear=2007",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Convergence Information
Technology (2nd: 2007: Kyongju-si, Korea)",
subject = "computer science; congresses; information technology",
}
@Proceedings{Ock:2007:ASI,
editor = "CheolYoung Ock and JeongYong Byun and YuDe Bi",
booktitle = "{ALPIT 2007: Sixth International Conference on
Advanced Language Processing and Web Information
Technology: proceedings: August 22--24, 2007, Luoyang,
Henan, China}",
title = "{ALPIT 2007: Sixth International Conference on
Advanced Language Processing and Web Information
Technology: proceedings: August 22--24, 2007, Luoyang,
Henan, China}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xix + 615",
year = "2007",
ISBN = "0-7695-2930-5",
ISBN-13 = "978-0-7695-2930-1",
LCCN = "QA76.9.S88 2007",
bibdate = "Thu May 6 16:51:10 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4460594",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Advanced Language
Processing and Web Information Technology (6th: 2007:
Luoyang, Henan Sheng, China)",
subject = "system design; congresses; parallel processing
(electronic computers)",
}
@Proceedings{Ramakrishnan:2007:PSI,
editor = "Naren Ramakrishnan and others",
booktitle = "{Proceedings of the Seventh IEEE International
Conference on Data Mining: ICDM 2007: 28--31 October,
2007, Omaha, Nebraska}",
title = "{Proceedings of the Seventh IEEE International
Conference on Data Mining: ICDM 2007: 28--31 October,
2007, Omaha, Nebraska}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxviii + 771",
year = "2007",
ISBN = "0-7695-3018-4",
ISBN-13 = "978-0-7695-3018-5",
LCCN = "QA76.9.D343 I133 2007",
bibdate = "Thu May 6 16:11:47 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
note = "IEEE Computer Society order number P3018.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4470209",
acknowledgement = ack-nhfb,
}
@Proceedings{Tjoa:2007:DIC,
editor = "A. Min Tjoa and Roland R. Wagner",
booktitle = "{DEXA 2007: 18th International Conference on Database
and Expert Systems Applications: proceedings:
Regensburg, Germany, 3--7 September, 2007}",
title = "{DEXA 2007: 18th International Conference on Database
and Expert Systems Applications: proceedings:
Regensburg, Germany, 3--7 September, 2007}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xix + 863",
year = "2007",
ISBN = "0-7695-2932-1",
ISBN-13 = "978-0-7695-2932-5",
LCCN = "QA76.9.D3 2007",
bibdate = "Thu May 6 10:42:07 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P2932.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4312838",
acknowledgement = ack-nhfb,
meetingname = "International Workshop on Database and Expert Systems
Applications (18th: 2007: Regensberg, Germany)",
subject = "database management; congresses; expert systems
(computer science)",
}
@Proceedings{ACM:2008:PNA,
editor = "{ACM}",
booktitle = "{Proceedings of the Nineteenth Annual ACM-SIAM
Symposium on Discrete Algorithms: [San Francisco, CA,
January 20--22, 2008]}",
title = "{Proceedings of the Nineteenth Annual ACM-SIAM
Symposium on Discrete Algorithms: [San Francisco, CA,
January 20--22, 2008]}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "xvi + 1279",
year = "2008",
ISBN = "0-89871-647-0",
ISBN-13 = "978-0-89871-647-4",
LCCN = "QA76.9.A43 A34 2008",
bibdate = "Thu May 6 10:50:27 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
acknowledgement = ack-nhfb,
}
@Proceedings{Aiello:2008:AMW,
editor = "William Anthony Aiello and others",
booktitle = "{Algorithms and models for the web-graph: fourth
international workshop, WAW 2006, Banff, Canada,
November 30--December 1, 2006: revised papers}",
title = "{Algorithms and models for the web-graph: fourth
international workshop, WAW 2006, Banff, Canada,
November 30--December 1, 2006: revised papers}",
volume = "4936",
publisher = pub-SV,
address = pub-SV:adr,
pages = "x + 165",
year = "2008",
ISBN = "3-540-78808-5, 3-540-78807-7",
ISBN-13 = "978-3-540-78808-9, 978-3-540-78807-2",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.9.A43 W39 2006",
bibdate = "Thu May 6 08:22:51 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
series = ser-LNCS,
acknowledgement = ack-nhfb,
meetingname = "WAW 2006 (2006: Banff, Alta.)",
subject = "computer algorithms; congresses; data mining; computer
science; data mining and knowledge discovery;
information systems applications (including Internet)",
}
@Proceedings{IEEE:2008:ICD,
editor = "{IEEE}",
booktitle = "{47th IEEE Conference on Decision and Control, 2008.
CDC 2008. 9--11 December 2008, Cancun, Mexico}",
title = "{47th IEEE Conference on Decision and Control, 2008.
CDC 2008. 9--11 December 2008, Cancun, Mexico}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2008",
ISBN = "1-4244-3123-9",
ISBN-13 = "978-1-4244-3123-6",
LCCN = "????",
bibdate = "Thu May 6 10:51:18 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4721212",
acknowledgement = ack-nhfb,
}
@Proceedings{IEEE:2008:PIC,
editor = "{IEEE}",
booktitle = "{Proceedings of the International Conference on
Computer Science and Information Technology: August
29--September 2, 2008, Singapore. ICCSIT '08}",
title = "{Proceedings of the International Conference on
Computer Science and Information Technology: August
29--September 2, 2008, Singapore. ICCSIT '08}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xxiii + 994",
year = "2008",
ISBN = "0-7695-3308-6",
ISBN-13 = "978-0-7695-3308-7",
LCCN = "QA75.5 2008",
bibdate = "Thu May 6 09:42:05 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P3308.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4624812",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Computer Science and
Information Technology (2008: Singapore)",
subject = "computer science; congresses; information technology",
}
@Proceedings{IEEE:2008:PII,
editor = "{IEEE}",
booktitle = "{Proceedings of 2008 IEEE International Conference on
Networking, Sensing, and Control: Sanya, China, April
6--8, 2008}",
title = "{Proceedings of 2008 IEEE International Conference on
Networking, Sensing, and Control: Sanya, China, April
6--8, 2008}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1855",
year = "2008",
ISBN = "1-4244-1685-X",
ISBN-13 = "978-1-4244-1685-1",
LCCN = "TK5105.5 2008",
bibdate = "Thu May 6 09:44:41 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE catalog number CFP08NSC-PRT.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4489617",
acknowledgement = ack-nhfb,
meetingname = "IEEE International Conference on Networking, Sensing,
and Control (2008: Sanya Shi, China)",
subject = "computer networks; congresses; detectors; control
theory; artificial intelligence; transportation; mobile
communication systems",
}
@Proceedings{Lenzerini:2008:PTS,
editor = "Maurizio Lenzerini and Domenico Lembo",
booktitle = "{Proceedings of the Twenty-Seventh ACM
SIGMOD-SIGACT-SIGART Symposium on Principles of
Database Systems: PODS'08, Vancouver, BC, Canada, June
9--11, 2008}",
title = "{Proceedings of the Twenty-Seventh ACM
SIGMOD-SIGACT-SIGART Symposium on Principles of
Database Systems: PODS'08, Vancouver, BC, Canada, June
9--11, 2008}",
publisher = pub-ACM,
address = pub-ACM:adr,
pages = "xi + 313",
year = "2008",
ISBN = "1-59593-685-8",
ISBN-13 = "978-1-59593-685-1",
LCCN = "????",
bibdate = "Fri Jun 20 13:10:29 MDT 2008",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
acknowledgement = ack-nhfb,
meetingname = "ACM SIGACT-SIGMOD-SIGART Symposium on Principles of
Database Systems (26th: 2007: Beijing, China)",
xxnote = "Check ISBN: OCLC has it assigned to the 26th
conference in Beijing.",
}
@Proceedings{Ma:2008:FFI,
editor = "Jun Ma and others",
booktitle = "{FSKD 2008: Fifth International Conference on Fuzzy
Systems and Knowledge Discovery: 18--20 October, 2008:
Jinan, Shandong, China. FSKD '08}",
title = "{FSKD 2008: Fifth International Conference on Fuzzy
Systems and Knowledge Discovery: 18--20 October, 2008:
Jinan, Shandong, China. FSKD '08}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "xix + 658 (vol. 1), xix + 642 (vol. 2), xiv + 687
(vol. 3), xx + 706 (vol. 4), xiv + 697 (vol. 5)",
year = "2008",
ISBN = "0-7695-3305-1",
ISBN-13 = "978-0-7695-3305-6",
LCCN = "QA248 2008; TJ212.2 .I143 2008",
bibdate = "Thu May 6 09:47:01 MDT 2010",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "Five volumes. IEEE Computer Society order number
P3305.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=4665920",
acknowledgement = ack-nhfb,
meetingname = "International Conference on Fuzzy Systems and
Knowledge Discovery (5th: 2008: Jinan, Shandong Sheng,
China)",
subject = "fuzzy systems; congresses; expert systems (computer
science)",
}
@Proceedings{Avrachenkov:2009:AMW,
editor = "Konstantin E. Avrachenkov and Debora Donato and Nelly
Litvak",
booktitle = "{Algorithms and models for the web-graph: 6th
international workshop, WAW 2009 Barcelona, Spain,
February 12--13, 2009 proceedings}",
title = "{Algorithms and models for the web-graph: 6th
international workshop, WAW 2009 Barcelona, Spain,
February 12--13, 2009 proceedings}",
volume = "5427",
publisher = pub-SV,
address = pub-SV:adr,
pages = "x + 183",
year = "2009",
ISBN = "3-540-95995-5, 3-540-95994-7 (softcover)",
ISBN-13 = "978-3-540-95995-3, 978-3-540-95994-6 (softcover)",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.9.A43 W39 2009",
bibdate = "Thu May 6 17:32:37 MDT 2010",
bibsource = "felix.us.ohio-state.edu:210/INNOPAC;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
series = ser-LNCS,
acknowledgement = ack-nhfb,
}
@Proceedings{Deng:2009:FAT,
editor = "Xiaotie Deng and John E. Hopcroft and Jinyun Xue",
booktitle = "{Frontiers in algorithmics. Third international
workshop, FAW 2009, Hefei, China, June 20--23, 2009.
Proceedings}",
title = "{Frontiers in algorithmics. Third international
workshop, FAW 2009, Hefei, China, June 20--23, 2009.
Proceedings}",
volume = "5598",
publisher = pub-SV,
address = pub-SV:adr,
pages = "xiv + 372",
year = "2009",
DOI = "https://doi.org/10.1007/978-3-642-02270-8",
ISBN = "3-642-02270-7, 3-642-02269-3",
ISBN-13 = "978-3-642-02270-8, 978-3-642-02269-2",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.9.A43 F39 2009",
bibdate = "Thu May 6 11:19:59 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
series = ser-LNCS,
URL = "http://www.springerlink.com/content/p1040n9jt618;
http://www.zentralblatt-math.org/zmath/en/search/?an=1166.68003",
acknowledgement = ack-nhfb,
subject = "algorithms; computational complexity; computer
communication networks; computer science; computer
software; data mining; software engineering",
xxeditor = "David Hutchison and Bernhard Steffen and Doug Tygar
and Takeo Kanade and Josef Kittler and Jon M. Kleinberg
and Friedemann Mattern and John C. Mitchell and Jinyun
Xue and Oscar Nierstrasz and Madhu Sudan and John E.
Hopcroft and Xiaotie Deng and Gerhard Weikum and Moshe
Y. Vardi and C. {Pandu Rangan} and Demetri Terzopoulos
and Moni Nao",
}
@Proceedings{IEEE:2009:IIC,
editor = "{IEEE}",
booktitle = "{IEEE International Conference on Fuzzy Systems, 2009:
FUZZ-IEEE 2009. 20--24 Aug. 2009, ICC Jeju, Jeju
Island, Korea}",
title = "{IEEE International Conference on Fuzzy Systems, 2009:
FUZZ-IEEE 2009. 20--24 Aug. 2009, ICC Jeju, Jeju
Island, Korea}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2009",
ISBN = "1-4244-3596-X",
ISBN-13 = "978-1-4244-3596-8",
LCCN = "????",
bibdate = "Thu May 6 10:00:09 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=5247842",
acknowledgement = ack-nhfb,
remark = "Parallel als Druckausg. erschienen.",
}
@Proceedings{IEEE:2009:PWW,
editor = "{IEEE}",
booktitle = "{Proceedings of the 2009 WRI World Congress on
Computer Science and Information Engineering: 31
March--2 April 2009, Los Angeles, California USA}",
title = "{Proceedings of the 2009 WRI World Congress on
Computer Science and Information Engineering: 31
March--2 April 2009, Los Angeles, California USA}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "????",
year = "2009",
ISBN = "0-7695-3507-0",
ISBN-13 = "978-0-7695-3507-4",
LCCN = "QA75.5 2009",
bibdate = "Thu May 6 09:49:49 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
melvyl.cdlib.org:210/CDL90",
note = "IEEE Computer Society order number P3507.",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=5170260",
acknowledgement = ack-nhfb,
meetingname = "WRI World Congress on Computer Science and Information
Engineering (2009: Los Angeles, Calif.)",
subject = "computer science; congresses",
}
@Book{Rousseau:2009:MT,
editor = "Christiane Rousseau and Yvan Saint-Aubin",
booktitle = "Math{\'e}matiques et Technologie",
title = "Math{\'e}matiques et Technologie",
publisher = pub-SV,
address = pub-SV:adr,
pages = "????",
year = "2009",
DOI = "https://doi.org/10.1007/978-0-387-69213-5",
ISBN = "0-387-69213-4",
ISBN-13 = "978-0-387-69213-5",
LCCN = "????",
bibdate = "Tue Jul 20 16:39:29 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
series = "Springer Undergraduate Texts in Mathematics and
Technology",
URL = "http://d-nb.info/997902213/34;
http://nbn-resolving.de/urn:nbn:de:1111-20091103138;
http://www.springerlink.com/content/r61844",
acknowledgement = ack-nhfb,
language = "French",
subject = "Computer science; Distribution (Probability theory);
Mathematics; PageRank",
}
@Proceedings{Sohn:2009:FIC,
editor = "Sungwon Sohn",
booktitle = "{2009 Fourth International Conference on Computer
Sciences and Convergence Information Technology: (ICCIT
2009). Seoul, Korea, 24--26 November 2009}",
title = "{2009 Fourth International Conference on Computer
Sciences and Convergence Information Technology: (ICCIT
2009). Seoul, Korea, 24--26 November 2009}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
year = "2009",
ISBN = "1-4244-5244-9",
ISBN-13 = "978-1-4244-5244-6",
LCCN = "????",
bibdate = "Thu May 6 10:56:31 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=5367867",
acknowledgement = ack-nhfb,
}
@Proceedings{Yu:2009:AFS,
editor = "Jian Yu",
booktitle = "{Advances in fuzzy sets and knowledge discovery: [The
Fourth International Conference on Fuzzy Systems and
Knowledge Discovery (FSKD'07) was held \ldots{} from
24--27 August 2007 in Haikou, Hainan, China]}",
title = "{Advances in fuzzy sets and knowledge discovery: [The
Fourth International Conference on Fuzzy Systems and
Knowledge Discovery (FSKD'07) was held \ldots{} from
24--27 August 2007 in Haikou, Hainan, China]}",
volume = "57.2009,6",
publisher = pub-ELSEVIER,
address = pub-ELSEVIER:adr,
pages = "865--1072",
year = "2009",
LCCN = "????",
bibdate = "Thu May 6 10:43:28 MDT 2010",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.gbv.de:20011/gvk",
series = "Computers and mathematics with applications",
acknowledgement = ack-nhfb,
}
@Article{Du:2012:SDS,
author = "Donglei Du and Connie F. Lee and Xiu-Qing Li",
title = "Systematic Differences in Signal Emitting and
Receiving Revealed by {PageRank} Analysis of a Human
Protein Interactome",
journal = j-PLOS-ONE,
volume = "7",
number = "9",
pages = "e44872:1--e44872:9",
month = sep,
year = "2012",
CODEN = "POLNCL",
DOI = "https://doi.org/10.1371/journal.pone.0044872",
ISSN = "1932-6203",
bibdate = "Wed Aug 12 08:36:35 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
URL = "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0044872",
abstract = "Most protein PageRank studies do not use signal flow
direction information in protein interactions because
this information was not readily available in large
protein databases until recently. Therefore, four
questions have yet to be answered: (A) What is the
general difference between signal emitting and
receiving in a protein interactome? (B) Which proteins
are among the top ranked in directional ranking? (C)
Are high ranked proteins more evolutionarily conserved
than low ranked ones? (D) Do proteins with similar
ranking tend to have similar subcellular locations? In
this study, we address these questions using the
forward, reverse, and non-directional PageRank
approaches to rank an information-directional network
of human proteins and study their evolutionary
conservation. The forward ranking gives credit to
information receivers, reverse ranking to information
emitters, and non-directional ranking mainly to the
number of interactions. The protein lists generated by
the forward and non-directional rankings are highly
correlated, but those by the reverse and
non-directional rankings are not. The results suggest
that the signal emitting/receiving system is
characterized by key-emittings and relatively even
receivings in the human protein interactome. Signaling
pathway proteins are frequent in top ranked ones. Eight
proteins are both informational top emitters and top
receivers. Top ranked proteins, except a few
species-related novel-function ones, are evolutionarily
well conserved. Protein-subunit ranking position
reflects subunit function. These results demonstrate
the usefulness of different PageRank approaches in
characterizing protein networks and provide insights to
protein interaction in the cell.",
acknowledgement = ack-nhfb,
fjournal = "PLoS One",
journal-URL = "http://www.plosone.org/",
}
@Book{Rebaza:2012:FCA,
author = "Jorge Rebaza",
booktitle = "A first course in applied mathematics",
title = "A first course in applied mathematics",
publisher = pub-WILEY,
address = pub-WILEY:adr,
pages = "xvi + 439",
year = "2012",
ISBN = "1-118-22962-2",
ISBN-13 = "978-1-118-22962-0",
LCCN = "TA342 .R43 2012",
bibdate = "Tue May 5 16:13:00 MDT 2015",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/mathgaz2010.bib;
https://www.math.utah.edu/pub/tex/bib/numana2010.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
z3950.loc.gov:7090/Voyager",
URL = "http://www.loc.gov/catdir/enhancements/fy1201/2011043340-d.html;
http://www.loc.gov/catdir/enhancements/fy1201/2011043340-t.html;
http://www.loc.gov/catdir/enhancements/fy1210/2011043340-b.html",
abstract = "This book details how applied mathematics involves
predictions, interpretations, analysis, and
mathematical modeling to solve real-world problems. Due
to the broad range of applications, mathematical
concepts and techniques and reviewed throughout,
especially those in linear algebra, matrix analysis,
and differential equations. Some classical definitions
and results from analysis are also discussed and used.
Some applications (postscript fonts, information
retrieval, etc.) are presented at the end of a chapter
as an immediate application of the theory just covered,
while those applications that are discussed in more
detail (ranking web pages, compression, etc.) are
presented in dedicated chapters. A collection of
mathematical models of a slightly different nature,
such as basic discrete mathematics and optimization, is
also provided. Clear proofs of the main theorems
ultimately help to make the statements of the theorems
more understandable, and a multitude of examples follow
important theorems and concepts. In addition, the
author builds material from scratch and thoroughly
covers the theory needed to explain the applications in
full detail, while not overwhelming readers with
unnecessary topics or discussions. In terms of
exercises, the author continuously refers to the real
numbers and results in calculus when introducing a new
topic so readers can grasp the concept of the otherwise
intimidating expressions. By doing this, the author is
able to focus on the concepts rather than the rigor.
The quality, quantity, and varying level of difficulty
of the exercises provides instructors more classroom
flexibility. Topical coverage includes linear algebra;
ranking web pages; matrix factorizations; least
squares; image compression; ordinary differential
equations; dynamical systems; and mathematical
models.",
acknowledgement = ack-nhfb,
author-dates = "1962--",
subject = "Mathematical models; Computer simulation; Mathematics
/ Applied",
tableofcontents = "Preface / xi \\
1. Basics of Linear Algebra / 1 \\
1.1 Notation and Terminology / 1 \\
1.2 Vector and Matrix Norms / 4 \\
1.3 Dot Product and Orthogonality / 8 \\
1.4 Special Matrices / 9 \\
1.5 Vector Spaces / 21 \\
1.6 Linear Independence and Basis / 24 \\
1.7 Orthogonalization and Direct Sums / 30 \\
1.8 Column Space, Row Space and Null Space / 34 \\
1.9 Orthogonal Projections / 43 \\
1.10 Eigenvalues and Eigenvectors / 47 \\
1.11 Similarity / 56 \\
1.12 Bezier Curves Postscripts Fonts / 59 \\
1.13 Final Remarks and Further Reading / 68 \\
2. Ranking Web Pages / 79 \\
2.1 The Power Method / 80 \\
2.2 Stochastic, Irreducible and Primitive Matrices / 84
\\
2.3 Google's PageRank Algorithm / 92 \\
2.4 Alternatives to Power Method / 106 \\
2.5 Final Remarks and Further Reading / 120 \\
3. Matrix Factorizations / 131 \\
3.1 LU Factorization / 132 \\
3.2 QR Factorization / 142 \\
3.3 Singular Value Decomposition (SVD) / 155 \\
3.4 Schur Factorization / 166 \\
3.5 Information Retrieval / 186 \\
3.6 Partition of Simple Substitution Cryptograms / 194
\\
3.7 Final Remarks and Further Reading / 203 \\
4. Least Squares / 215 \\
4.1 Projections and Normal Equations / 215 \\
4.2 Least Squares and QR Factorization / 224 \\
4.3 Lagrange Multipliers / 228 \\
4.4 Final Remarks and Further Reading / 231 \\
5. Image Compression / 235 \\
5.1 Compressing with Discrete Cosine Transform / 236
\\
5.2 Huffman Coding / 260 \\
5.3 Compression with SVD / 267 \\
5.4 Final Remarks and Further Reading / 271 \\
6. Ordinary Differential Equations / 277 \\
6.1 One-Dimensional Differential Equations / 278 \\
6.2 Linear Systems of Differential Equations / 307 \\
6.3 Solutions via Eigenvalues and Eigenvectors / 308
\\
6.4 Fundamentals Matrix Solution / 312 \\
6.5 Final Remarks and Further Reading / 316 \\
7. Dynamical Systems / 325 \\
7.1 Linear Dynamical Systems / 326 \\
7.2 Nonlinear Dynamical Systems / 340 \\
7.3 Predator--Prey Models with Harvesting / 374 \\
7.4 Final Remarks and Further Reading / 385 \\
8. Mathematical Models / 395 \\
8.1 Optimization of a Waste Management System / 396 \\
8.2 Grouping Problem in Networks / 404 \\
8.3 American Cutaneous Leishmaniasis / 410 \\
8.4 Variable Population Interactions / 420 \\
References / 431 \\
Index / 435",
}
@Book{Pitici:2019:BWM,
editor = "Mircea Pitici",
booktitle = "The Best Writing On Mathematics: 2019",
title = "The Best Writing On Mathematics: 2019",
volume = "2019",
publisher = pub-PRINCETON,
address = pub-PRINCETON:adr,
pages = "xvi + 272 + 16",
year = "2019",
ISBN = "0-691-19835-7, 0-691-19867-5",
ISBN-13 = "978-0-691-19835-4, 978-0-691-19867-5",
LCCN = "QA8.6 .B337 2019",
bibdate = "Mon Dec 9 05:55:58 MST 2019",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/kepler.bib;
https://www.math.utah.edu/pub/tex/bib/master.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
abstract = "An anthology of the year's finest writing on
mathematics from around the world, featuring promising
new voices as well as some of the foremost names in
mathematics.",
acknowledgement = ack-nhfb,
subject = "Mathematics; Popular works; Mathematics.",
tableofcontents = "Introduction / Mircea Pitici / ix--xvi \\
Geometry v. gerrymandering / Moon Duchin / 1--11 \\
Slicing sandwiches, states, and solar systems: can
mathematical tools help determine what divisions are
provably fair? / Theodore P. Hill / 12--26 \\
Does mathematics teach how to think? / Paul J. Campbell
/ 27--42 \\
Abstracting the Rubik's cube / Roice Nelson / 43--52
\\
Topology-disturbing objects: a new class of 3D optical
illusion / Kokichi Sugihara / 53--73 \\
Mathematicians explore mirror link between two
geometric worlds / Kevin Hartnett / 74--80 \\
Professor Engel's marvelously improbable machines /
James Propp / 81--89 \\
The on-line encyclopedia of integer sequences / Neil J.
A. Sloane / 90--119 \\
Mathematics for big data / Alessandro Di Bucchianico,
Laura Iapichino, Nelly Litvak, Frank van der Meulen,
and Ron Wehrens / 120--131 \\
The un(solv)able problem / Toby S. Cubitt, David
P{\'e}rez-Garc{\'i}a, and Michael Wolf / 132--149 \\
The mechanization of mathematics / Jeremy Avigad /
150--170 \\
Mathematics as an empirical phenomenon, subject to
modeling / Reuben Hersh / 171--185 \\
Does $2 + 3 = 5$? In defense of a near absurdity / Mary
Leng / 186--194 \\
Gregory's sixth operation / Tiziana Bascelli, Piotr
Blaszczyk, Validmir Kanovei, Karin U. Katz, Mikhail G.
Katz, Semen S. Kutateladze, Tahl Nowik, Daivd M.
Schaps, and David Sherry / 195--207 \\
Kolmogorov complexity and our search for meaning: what
math can teach us about finding order in our chaotic
lives / Noson S. Yanofsky / 208--213 \\
Ethics in statistical practice and communication: five
recommendations / Andrew Gelman / 214--223 \\
The Fields Medal should return to its roots / Michael
J. Barany / 224--231 \\
The Erd{\H{o}}s paradox / Melvyn B. Nathanson /
232--239 \\
Contributors / 241--249 \\
Notable Writings / 251--268 \\
Acknowledgments / 269--270 \\
Credits [to original publication of this book's
chapters] / 271--272",
}