%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.167", %%% date = "30 September 2024", %%% time = "12:33:24 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 = "21253 19123 90813 907230", %%% 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 1.167, 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 ( 6) %%% 1967 ( 0) 1996 ( 0) 2025 ( 1) %%% 1968 ( 0) 1997 ( 1) %%% 1969 ( 0) 1998 ( 2) %%% %%% Article: 279 %%% Book: 20 %%% InBook: 4 %%% InCollection: 6 %%% InProceedings: 224 %%% MastersThesis: 1 %%% Misc: 7 %%% PhdThesis: 2 %%% Proceedings: 81 %%% TechReport: 11 %%% %%% Total entries: 635 %%% %%% 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.", %%% } %%% ====================================================================

@Preamble{ "\ifx \undefined \booktitle \def \booktitle#1{{{\em #1}}} \fi" }

%%% ==================================================================== %%% Institution abbreviations:

@String{inst-MATHWORKS= "The MathWorks, Inc."} @String{inst-MATHWORKS:adr= "3 Apple Hill Drive, Natick, MA 01760-2098, USA"}

%%% ==================================================================== %%% Journal abbreviations:

@String{j-ALGORITHMICA= "Algorithmica"} @String{j-AMER-MATH-MONTHLY= "American Mathematical Monthly"} @String{j-ANN-APPL-PROBAB= "Annals of Applied Probability"} @String{j-APPL-MATH-COMP= "Applied Mathematics and Computation"} @String{j-APPL-NUM-MATH= "Applied Numerical Mathematics: Transactions of IMACS"} @String{j-APPL-MATH-LETT= "Applied Mathematics Letters"} @String{j-BIT= "BIT (Nordisk tidskrift for informationsbehandling)"} @String{j-BMC-BIOINFORMATICS= "BMC Bioinformatics"} @String{j-CACM= "Communications of the ACM"} @String{j-CCPE= "Concurrency and Computation: Prac\-tice and Experience"} @String{j-COMP-J= "The Computer Journal"} @String{j-COMP-NET-ISDN= "Computer Networks and ISDN Systems"} @String{j-COMP-NET-AMSTERDAM= "Computer Networks (Amsterdam, Netherlands: 1999)"} @String{j-COMP-SURV= "ACM Computing Surveys"} @String{j-COMPUT-MATH-APPL= "Computers and Mathematics with Applications"} @String{j-COMPUTERS-AND-GRAPHICS= "Computers and Graphics"} @String{j-C-R-MATH-ACAD-SCI-PARIS= "Comptes Rendus Math{\'e}matique. Acad{\'e}mie des Sciences. Paris"} @String{j-DOKL-AKAD-NAUK= "Doklady Akademii nauk SSSR"} @String{j-DYN-CONTIN-DISCR-IMPULS-B= "Dynamics of Continuous, Discrete \& Impulsive Systems. Series B. Applications \& Algorithms"} @String{j-ELECTRON-TRANS-NUMER-ANAL= "Electronic Transactions on Numerical Analysis (ETNA)"} @String{j-FUND-INFO= "Fundamenta Informaticae"} @String{j-FUT-GEN-COMP-SYS= "Future Generation Computer Systems"} @String{j-IEEE-ANN-HIST-COMPUT= "IEEE Annals of the History of Computing"} @String{j-IEEE-INTERNET-COMPUT= "IEEE Internet Computing"} @String{j-IEEE-TRANS-AUTOMAT-CONTR= "IEEE Transactions on Automatic Control"} @String{j-IEEE-TRANS-KNOWL-DATA-ENG= "IEEE Transactions on Knowledge and Data Engineering"} @String{j-IEEE-TRANS-PAR-DIST-SYS= "IEEE Transactions on Parallel and Distributed Systems"} @String{j-IEEE-TRANS-PATT-ANAL-MACH-INTEL= "IEEE Transactions on Pattern Analysis and Machine Intelligence"} @String{j-IEEE-TRANS-SOFTW-ENG= "IEEE Transactions on Software Engineering"} @String{j-INF-RETR= "Information Retrieval"} @String{j-INFO-PROC-LETT= "Information Processing Letters"} @String{j-INFORM-THEOR-APPL= "Informatique th{\'e}orique et applications := Theoretical informatics and applications"} @String{j-INT-J-BIFURC-CHAOS-APPL-SCI-ENG= "International journal of bifurcation and chaos in applied sciences and engineering"} @String{j-INT-J-COMP-PROC-ORIENTAL-LANG= "International Journal of Computer Processing of Oriental Languages (IJCPOL)"} @String{j-INT-J-PARALLEL-PROG= "International Journal of Parallel Programming"} @String{j-INTERNET-MATH= "Internet Mathematics"} @String{j-J-ACM= "Journal of the ACM"} @String{j-J-AM-SOC-INF-SCI-TECHNOL= "Journal of the American Society for 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"} @String{j-J-COMPUT-BIOL= "Journal of Computational Biology"} @String{j-J-GRID-COMP= "Journal of Grid Computing"} @String{j-J-INFORMETRICS= "Journal of Informetrics"} @String{j-J-MATH-CHEM= "Journal of Mathematical Chemistry"} @String{j-J-PAR-DIST-COMP= "Journal of Parallel and Distributed 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"} @String{j-J-STAT-PHYS= "Journal of Statistical Physics"} @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"} @String{j-NUM-LIN-ALG-APPL= "Numerical Linear Algebra with Applications"} @String{j-NUMER-ALGEBRA-CONTROL-OPTIM= "Numerical Algebra, Control and 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"} @String{j-PLOS-COMPUT-BIOL= "PLoS Computational Biology"} @String{j-PLOS-ONE= "PLoS One"} @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{Ma:2024:PPP, author = "Ke Ma and Jiawei Li and Mengyuan Zhao and Ibrahim Zamit and Bin Lin and Fei Guo and Jijun Tang", title = "{PPRTGI}: a Personalized {PageRank} Graph Neural Network for {TF}-Target Gene Interaction Detection", journal = j-TCBB, volume = "21", number = "3", pages = "480--491", month = may, year = "2024", CODEN = "ITCBCY", DOI = "https://doi.org/10.1109/TCBB.2024.3374430", ISSN = "1545-5963 (print), 1557-9964 (electronic)", ISSN-L = "1545-5963", bibdate = "Thu Sep 26 07:01:14 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tcbb.bib", URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3374430", abstract = "Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for \ldots{}", acknowledgement = ack-nhfb, ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.", fjournal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics", journal-URL = "https://dl.acm.org/loi/tcbb", } @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"