# bibtab

bibtype: article
filename: pagerank.bib
label: 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
pages: P06010
month: jun
monthnumber: 6
year: 2007
CODEN: JSMTC6
DOI: https://doi.org/10.1088/1742-5468/2007/06/P06010
ISSN: 1742-5468 OR 17425468
bibdate: Tue Aug 11 17:42:29 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:42:29 ???
URL: http://arxiv.org/abs/physics/0612122; http://iopscience.iop.org/1742-5468/2007/06/P06010/fulltext/
fjournal: Journal of Statistical Mechanics: Theory and Experiment
keywords: CiteRank
acknowledgement: ack-nhfb
journalURL: http://iopscience.iop.org/1742-5468/
rawauthor: Dylan Walker and Huafeng Xie and Koon-Kiu Yan and Sergei Maslov
rawtitle: Ranking scientific publications using a model of network traffic
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Wang:2007:KEB
author: Jinghua Wang and Jianyi Liu and Cong Wang
editor: Zhi-Hua Zhou and Hang Li and Qiang Yang
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 OR 3540717005
ISBN13: 978-3-540-71700-3 OR 9783540717003
bibdate: Sat May 8 18:33:07 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:07 MDT
series: ser-LNAI
acknowledgement: ack-nhfb
rawauthor: Jinghua Wang and Jianyi Liu and Cong Wang
raweditor: Zhi-Hua Zhou and Hang Li and Qiang Yang
rawtitle: Keyword extraction based on {PageRank}
booktitle:
PPAKDD'07: Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining

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.

rawabstract:
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.

rawbooktitle:
{PPAKDD'07: Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining}

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: Wicks:2007:MEP
author: John R. Wicks and Amy Greenwald
editor: Wessel Kraaij and Arjen P. de Vries
title: More efficient parallel computation of PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 861--862
year: 2007
ISBN: 1-59593-597-5 OR 1595935975
ISBN13: 978-1-59593-597-7 OR 9781595935977
LCCN: Z699.A1
bibdate: Sat May 8 18:33:11 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:11 MDT
keywords: pagerank; power iteration; web graph
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1145/1277741
bookpages: 928
rawauthor: John R. Wicks and Amy Greenwald
raweditor: Wessel Kraaij and Arjen P. de Vries
rawtitle: More efficient parallel computation of {PageRank}
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

rawbooktitle:
{Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR2007. Amsterdam (the Netherlands), July 23--27, 2007}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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) OR 03029743 (print), 16113349 (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
bibtimestamp: 2010.05.05 19:28:06 ???
series: ser-LNCS
ZMnumber: 1136.68340
acknowledgement: ack-nhfb
rawauthor: John Wicks and Amy Greenwald
rawtitle: Parallelizing the computation of {PageRank}
entry:
@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,
}


bibtype: article
filename: pagerank.bib
label: 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) OR 10705325 (print), 10991506 (electronic)
ISSNL: 1070-5325 OR 10705325
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
bibtimestamp: 2010.05.05 19:28:06 ???
fjournal: Numerical Linear Algebra with Applications
ZMnumber: 05596057
acknowledgement: ack-nhfb
journalURL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506
rawauthor: Gang Wu and Yimin Wei
rawtitle: A Power-{Arnoldi} algorithm for computing {PageRank}
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2010.05.06 15:38:58 ???
acknowledgement: ack-nhfb
rawauthor: Gang Wu and Juanzi Li
rawtitle: {SWRank}: An Approach for Ranking {Semantic Web} Reversely and Consistently
entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4806427
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4801442
rawauthor: Lun Yang and Bin Wang and Gongli Xia and Zhenhua Xia and Langlai Xu
rawbooktitle: {BIC-TA 2007: Second International Conference on Bio-Inspired Computing: Theories and Applications}
rawtitle: Bibliomics-based Selection of Analgesics Targets through {Google}-{PageRank}-like Algorithm
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Yang:2007:DPP
author: Haixuan Yang and Irwin King and Michael R. Lyu
editor: Wessel Kraaij and Arjen P. de Vries
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 OR 1595935975
ISBN13: 978-1-59593-597-7 OR 9781595935977
LCCN: Z699.A1
bibdate: Tue Aug 11 17:50:04 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:50:04 ???
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1145/1277741
bookpages: 928
rawauthor: Haixuan Yang and Irwin King and Michael R. Lyu
raweditor: Wessel Kraaij and Arjen P. de Vries
rawtitle: {DiffusionRank}: A possible penicillin for web spamming
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

rawbooktitle:
{Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR2007. Amsterdam (the Netherlands), July 23--27, 2007}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Yuan:2007:IPF
author: Fuyong Yuan and Chunxia Yin and Jian Liu
editor: IEEE
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 OR 0769529097
ISBN13: 978-0-7695-2909-7 OR 9780769529097
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4287791
keywords: focused crawler; PageRank; T-PageRank; topical random surfer
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4287452
rawauthor: Fuyong Yuan and Chunxia Yin and Jian Liu
raweditor: {IEEE}
rawtitle: Improvement of {PageRank} for Focused Crawler
booktitle:
SNPD 2007: Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel / Distributed Computing

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.

rawabstract:
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.

rawbooktitle:
{SNPD 2007: Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel\slash Distributed Computing}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2010.02.19 18:09:30 ???
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4287452
rawauthor: Fuyong Yuan and Chunxia Yin and Jian Liu
rawtitle: {PageRank} for Focused Crawler
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2010.05.06 16:52:48 ???
acknowledgement: ack-nhfb
rawauthor: BaoJun Yue and Heng Liang and Fengshan Bai
rawtitle: Understanding the {GeneRank} Model
entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4406097
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4405868
rawauthor: Yulian Zhang and Chunxia Yin and Fuyong Yuan
rawbooktitle: {FSKD 2007: Fourth International Conference on Fuzzy Systems and Knowledge Discovery}
rawtitle: An Application of Improved {PageRank} in Focused Crawler
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.

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
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 OR 3540714944
ISBN13: 978-3-540-71494-1 OR 9783540714941
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
bibdate: Sat May 8 18:33:08 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:08 MDT
series: ser-LNCS
acknowledgement: ack-nhfb
rawauthor: Li Zhang and Tao Qin and Tie-Yan Liu and Ying Bao and Hang Li
raweditor: Giambattista Amati and Claudio Carpineto and Giovanni Romano
rawtitle: {$N$}-step {PageRank} for {Web} search
booktitle:
Advances in information retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007: proceedings

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.

rawabstract:
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.

rawbooktitle:
{Advances in information retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007: proceedings}

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2010.05.07 17:05:21 ???
acknowledgement: ack-nhfb
rawauthor: Ding Zhou and S. A. Orshanskiy and Hongyuan Zha and C. L. Giles
rawtitle: Co-ranking Authors and Documents in a Heterogeneous Network
entry:
@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,
}


bibtype: article
filename: pagerank.bib
label: Andersen:2008:LCP
title: Local computation of PageRank contributions
journal: j-INTERNET-MATH
volume: 5
number: 1--2
pages: 23--45
year: 2008
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
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
bibtimestamp: 2010.05.05 19:28:06 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1259158596
fjournal: Internet Mathematics
ZMnumber: 1136.68316
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawtitle: Local computation of {PageRank} contributions
author:
Reid Andersen and Christian Borgs and Jennifer Chayes and John Hopcroft and Vahab Mirrokni and Shang-Hua Teng

rawauthor:
Reid Andersen and Christian Borgs and Jennifer Chayes and John Hopcroft and Vahab Mirrokni and Shang-Hua Teng

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
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
bibtimestamp: 2010.05.05 19:28:06 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1259158595
fjournal: Internet Mathematics
ZMnumber: 1136.68317
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Reid Andersen and Fan Chung and Kevin Lang
rawtitle: Local partitioning for directed graphs using {PageRank}
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Andersen:2008:RPL
editor: ACM
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 OR 1605581593
ISBN13: 978-1-60558-159-0 OR 9781605581590
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
keywords: directed graphs; graph algorithms; link spam; local algorithms; PageRank; unsupervised learning
acknowledgement: ack-nhfb
raweditor: {ACM}
rawtitle: Robust {PageRank} and locally computable spam detection features
author:
Reid Andersen and Christian Borgs and Jennifer Chayes and John Hopcroft and Kamal Jain and Vahab Mirrokni and Shanghua Teng

booktitle:
AIRWeb; Vol. 295 Proceedings of the 4th international workshop on Adversarial information retrieval on the web

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.

rawabstract:
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.

rawauthor:
Reid Andersen and Christian Borgs and Jennifer Chayes and John Hopcroft and Kamal Jain and Vahab Mirrokni and Shanghua Teng

rawbooktitle:
AIRWeb; Vol. 295 Proceedings of the 4th international workshop on Adversarial information retrieval on the web

entry:
@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",
}


bibtype: phdthesis
filename: pagerank.bib
label: 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
monthnumber: 9
year: 2008
ISBN: 0-549-92090-0 OR 0549920900
ISBN13: 978-0-549-92090-8 OR 9780549920908
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
note: Order Number AAI3338375.
acknowledgement: ack-nhfb
rawauthor: Christopher J. Augeri
rawnote: Order Number AAI3338375.
rawtitle: On graph isomorphism and the {PageRank} algorithm
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.

rawabstract:
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.

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: Avrachenkov:2008:PBC
editor: ACM
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 OR 160558164X
ISBN13: 978-1-60558-164-4 OR 9781605581644
bibdate: Sat May 8 18:33:05 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:05 MDT
keywords: directed graphs; PageRank based clustering
acknowledgement: ack-nhfb
raweditor: {ACM}
rawtitle: {PageRank} based clustering of hypertext document collections
author:
Konstantin Avrachenkov and Vladimir Dobrynin and Danil Nemirovsky and Son Kim Pham and Elena Smirnova

booktitle:
Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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.

rawabstract:
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.

rawauthor:
Konstantin Avrachenkov and Vladimir Dobrynin and Danil Nemirovsky and Son Kim Pham and Elena Smirnova

rawbooktitle:
Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
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
bibtimestamp: 2010.05.05 19:28:06 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1259158597
fjournal: Internet Mathematics
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Konstantin Avrachenkov and Nelly Litvak and Kim Son Pham
rawtitle: A singular perturbation approach for choosing the {PageRank} damping factor
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Bar-Yossef:2008:LAPa
author: Ziv Bar-Yossef and Li-Tal Mashiach
editor: ACM
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 OR 160558164X
ISBN13: 978-1-60558-164-4 OR 9781605581644
bibdate: Sat May 8 18:33:08 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:08 MDT
keywords: local approximation; lower bounds; PageRank; reverse PageRank
acknowledgement: ack-nhfb
rawauthor: Ziv Bar-Yossef and Li-Tal Mashiach
raweditor: {ACM}
rawtitle: Local approximation of {PageRank} and reverse {PageRank}
booktitle:
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

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.

rawabstract:
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.

rawbooktitle:
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Bar-Yossef:2008:LAPb
author: Ziv Bar-Yossef and Li-Tal Mashiach
editor: ACM
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 OR 1595939911
ISBN13: 978-1-59593-991-3 OR 9781595939913
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
keywords: local approximation; lower bounds; pagerank; reverse pagerank
acknowledgement: ack-nhfb
rawauthor: Ziv Bar-Yossef and Li-Tal Mashiach
raweditor: {ACM}
rawtitle: Local approximation of {PageRank} and {Reverse PageRank}
booktitle:
Conference on Information and Knowledge Management Proceeding of the 17th ACM conference on Information and knowledge management

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.

rawabstract:
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.

rawbooktitle:
Conference on Information and Knowledge Management Proceeding of the 17th ACM conference on Information and knowledge management

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 3540693203
ISBN13: 978-3-540-69320-8 OR 9783540693208
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (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
bibtimestamp: 2010.05.08 18:33:10 MDT
series: ser-LNCS
acknowledgement: ack-nhfb
rawauthor: Christian Bauckhage
rawbooktitle: Proceedings of the 30th DAGM Symposium on Pattern Recognition
raweditor: Gerhard Rigoll
rawtitle: Image Tagging Using {PageRank} over Bipartite Graphs
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.

rawabstract:
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.

entry:
@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,
}


bibtype: article
filename: pagerank.bib
label: 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 / 2009
ISSN: 1068-9613 (print), 1097-4067 (electronic) OR 10689613 (print), 10974067 (electronic)
ISSNL: 1068-9613 OR 10689613
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
bibtimestamp: 2010.09.06 12:28:30 MDT
URL: http://etna.mcs.kent.edu/vol.33.2008-2009/pp1-16.dir/pp1-16.pdf
fjournal: Electronic Transactions on Numerical Analysis
acknowledgement: ack-nhfb
rawauthor: Dario A. Bini and Gianna M. {Del Corso} and Francesco Romani
rawtitle: Evaluating scientific products by means of citation-based models: a first analysis and validation
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 3540788077
ISBN13: 978-3-540-78807-2 OR 9783540788072
MRclass: 68M10 68R10 68U35
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
ZMnumber: 1142.68309
acknowledgement: ack-nhfb
rawauthor: Paolo Boldi and Roberto Posenato and Massimo Santini and Sebastiano Vigna
rawtitle: Traps and Pitfalls of Topic-Biased {PageRank}
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.

rawabstract:
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.

entry:
@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,
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 7
year: 2008
CODEN: MCMPAF
DOI: https://doi.org/10.1090/S0025-5718-08-02086-3
ISSN: 0025-5718 (print), 1088-6842 (electronic) OR 00255718 (print), 10886842 (electronic)
ISSNL: 0025-5718 OR 00255718
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
bibtimestamp: 2008.07.08 06:24:30 MDT
fjournal: Mathematics of Computation
acknowledgement: ack-nhfb
journalURL: http://www.ams.org/mcom/
rawauthor: C. Brezinski and M. Redivo-Zaglia
rawtitle: Rational extrapolation for the {PageRank} vector
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

entry:
@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/",
}


bibtype: inproceedings
filename: pagerank.bib
label: Chebolu:2008:PRS
author: Prasad Chebolu and Pall 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
bibtimestamp: 2010.05.08 18:33:11 MDT
acknowledgement: ack-nhfb
rawauthor: Prasad Chebolu and P{\'a}ll Melsted
rawtitle: {PageRank} and the random surfer model
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.

rawabstract:
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.

entry:
@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,
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 9
year: 2008
CODEN: LAAPAW
DOI: https://doi.org/10.1016/j.laa.2008.01.023
ISSN: 0024-3795 (print), 1873-1856 (electronic) OR 00243795 (print), 18731856 (electronic)
ISSNL: 0024-3795 OR 00243795
MRclass: 15A18 (15A51 15A57 60J10 68U35)
MRnumber: MR2433177 (2009e:15030)
MRreviewer: Thomas H. Foregger
bibdate: Wed May 5 19:28:06 2010
bibtimestamp: 2010.05.05 19:28:06 ???
fjournal: Linear Algebra and its Applications
ZMnumber: 1147.68387
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00243795
rawauthor: Cristobald de Kerchove and Laure Ninove and Paul van Dooren
rawtitle: Maximizing {PageRank} via outlinks
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/00243795

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
year: 2008
CODEN: SJOCE3
DOI: https://doi.org/10.1137/070685142
ISSN: 1064-8275 (print), 1095-7197 (electronic) OR 10648275 (print), 10957197 (electronic)
ISSNL: 1064-8275 OR 10648275
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
bibtimestamp: 2010.05.19 10:44:08 MDT
fjournal: SIAM Journal on Scientific Computing
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sisc
rawauthor: H. {De Sterck} and Thomas A. Manteuffel and Stephen F. McCormick and Quoc Nguyen and John Ruge
rawtitle: Multilevel Adaptive Aggregation for {Markov} Chains, with Application to {Web} Ranking
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: Fiala:2008:PBN
author: Dalibor Fiala and Francois Rousselot and Karel Jezek
title: PageRank for bibliographic networks
journal: j-SCIENTOMETRICS
volume: 76
number: 1
pages: 135--158
month: may
monthnumber: 5
year: 2008
CODEN: SCNTDX
DOI: https://doi.org/10.1007/s11192-007-1908-4
ISSN: 0138-9130 (print), 1588-2861 (electronic) OR 01389130 (print), 15882861 (electronic)
ISSNL: 0138-9130 OR 01389130
bibdate: Tue Aug 11 16:41:10 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:41:10 ???
URL: http://link.springer.com/article/10.1007/s11192-007-1908-4
fjournal: Scientometrics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11192
rawauthor: Dalibor Fiala and Fran{\c{c}}ois Rousselot and Karel Je{\v{z}}ek
rawtitle: {PageRank} for bibliographic networks
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Fortunato:2008:APD
author: Santo Fortunato and Marian Boguna 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 OR 3540788077
ISBN13: 978-3-540-78807-2 OR 9783540788072
MRclass: 68M10 68U35 68P20
bibdate: Sat May 8 18:33:11 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:11 MDT
ZMnumber: 1142.68311
acknowledgement: ack-nhfb
rawauthor: Santo Fortunato and Mari{\'a}n Bogu{\~n}{\'a} and Alessandro Flammini and Filippo Menczer
rawtitle: Approximating {PageRank} from In-Degree
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.

rawabstract:
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.

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: Govan:2008:GGP
author: Anjela Y. Govan and Carl D. Meyer and Russell Albright
title: Generalizing Google's PageRank to rank national football league teams
publisher: pub-SAS
address: pub-SAS:adr
pages: ??--??
year: 2008
bibdate: Tue Aug 11 16:57:24 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:57:24 ???
note: SAS paper 151-2008.
URL: http://www2.sas.com/proceedings/forum2008/151-2008.pdf
acknowledgement: ack-nhfb
bookURL: http://www2.sas.com/proceedings/forum2008/TOC.html
rawauthor: Anjela Y. Govan and Carl D. Meyer and Russell Albright
rawnote: SAS paper 151-2008.
rawtitle: Generalizing {Google}'s {PageRank} to rank national football league teams
booktitle:
Proceedings of the SAS Global Forum 2008: March 16--19, 2008, Henry B. Gonzalez Convention Center, San Antonio, Texas

rawbooktitle:
{Proceedings of the SAS Global Forum 2008: March 16--19, 2008, Henry B. Gonzalez Convention Center, San Antonio, Texas}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Guo:2008:IBM
author: Chonghui Guo and Liang Zhang
editor: IEEE
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 OR 1424421071
ISBN13: 978-1-4244-2107-7 OR 9781424421077
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4680864
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4677908
rawauthor: Chonghui Guo and Liang Zhang
raweditor: {IEEE}
rawtitle: An Improved {BA} Model Based on the {PageRank} Algorithm
booktitle:
WiCOM '08. 4th International Conference on (Online) Wireless Communications, Networking and Mobile Computing, Dalian, China, 12--17 October 2008

rawbooktitle:
{WiCOM '08. 4th International Conference on (Online) Wireless Communications, Networking and Mobile Computing, Dalian, China, 12--17 October 2008}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Gupta:2008:FAT
author: Manish Gupta and Amit Pathak and Soumen Chakrabarti
editor: ACM
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 OR 1605580856
ISBN13: 978-1-60558-085-2 OR 9781605580852
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
keywords: HubRank; node-deletion; pagerank; personalized; top-$k$
acknowledgement: ack-nhfb
rawauthor: Manish Gupta and Amit Pathak and Soumen Chakrabarti
raweditor: {ACM}
rawtitle: Fast algorithms for top-$k$ personalized {PageRank} queries
booktitle:
International World Wide Web Conference Proceeding of the 17th international conference on World Wide Web

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.

rawabstract:
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.

rawbooktitle:
International World Wide Web Conference Proceeding of the 17th international conference on World Wide Web

entry:
@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-$k$",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 3
year: 2008
CODEN: ATDSD3
DOI: https://doi.org/10.1145/1331904.1331905
ISSN: 0362-5915 (print), 1557-4644 (electronic) OR 03625915 (print), 15574644 (electronic)
ISSNL: 0362-5915 OR 03625915
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
bibtimestamp: 2008.06.12 16:37:49 MDT
URL: https://www.math.utah.edu/pub/tex/bib/tods.bib
fjournal: ACM Transactions on Database Systems
keywords: Authority flow; PageRank; quality experiments; ranking; specificity
acknowledgement: ack-nhfb
articleno: 1
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J777
rawauthor: Vagelis Hristidis and Heasoo Hwang and Yannis Papakonstantinou
rawtitle: Authority-based keyword search in databases
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
year: 2008
CODEN: SJMAEL
DOI: https://doi.org/10.1137/060664331
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
bibdate: Tue May 18 22:32:22 MDT 2010
bibtimestamp: 2010.05.18 22:32:22 MDT
fjournal: SIAM Journal on Matrix Analysis and Applications
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Ilse C. F. Ipsen and Teresa M. Selee
rawtitle: {PageRank} Computation, with Special Attention to Dangling Nodes
bibsource:
http://epubs.siam.org/sam-bin/dbq/toclist/SIMAX/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4739020
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721212
rawauthor: H. Ishii and R. Tempo
rawbooktitle: {CDC 2008: 47th IEEE Conference on Decision and Control}
rawtitle: A distributed randomized approach for the {PageRank} computation: {Part 1}
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4739022
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721212
rawauthor: H. Ishii and R. Tempo
rawtitle: A distributed randomized approach for the {PageRank} computation: {Part 2}
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Jing:2008:PPI
author: Yushi Jing and Shumeet Baluja
editor: ACM
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 OR 1605580856
ISBN13: 978-1-60558-085-2 OR 9781605580852
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
keywords: graph theory; pagerank; visual similarity
acknowledgement: ack-nhfb
rawauthor: Yushi Jing and Shumeet Baluja
raweditor: {ACM}
rawtitle: {PageRank} for product image search
booktitle:
International World Wide Web Conference Proceeding of the 17th international conference on World Wide Web

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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 11
year: 2008
CODEN: ITPIDJ
DOI: https://doi.org/10.1109/TPAMI.2008.121
ISSN: 0162-8828 OR 01628828
ISSNL: 0162-8828 OR 01628828
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4522561
fjournal: IEEE Transactions on Pattern Analysis and Machine Intelligence
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34
journalURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34
rawauthor: Yushi Jing and S. Baluja
rawtitle: {VisualRank}: Applying {PageRank} to Large-Scale Image Search
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4624979
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4624812
rawauthor: M. Kale and P. S. Thilagam
rawbooktitle: {ICCSIT '08: International Conference on Computer Science and Information Technology}
rawtitle: {DYNA-RANK}: Efficient Calculation and Updation of {PageRank}
entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: Kaplan:2008:BRB
author: Daniel T. Kaplan
journal: j-AMER-MATH-MONTHLY
volume: 115
number: 8
pages: 765--768
month: oct
monthnumber: 10
year: 2008
CODEN: AMMYAE
ISSN: 0002-9890 (print), 1930-0972 (electronic) OR 00029890 (print), 19300972 (electronic)
ISSNL: 0002-9890 OR 00029890
bibdate: Mon Jan 30 12:00:31 MST 2012
bibtimestamp: 2012.01.30 12:00:31 MST
URL: http://www.jstor.org/stable/27642602
fjournal: American Mathematical Monthly
acknowledgement: ack-nhfb
journalURL: https://www.jstor.org/journals/00029890.htm
rawauthor: 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

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

rawtitle:
Book Review: {{\booktitle{Google}'s PageRank and Beyond: The Science of Search Engine Rankings}, by Amy N. Langville; Carl D. Meyer}

entry:
@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",
}


bibtype: techreport
filename: pagerank.bib
label: 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
bibtimestamp: 2015.08.11 16:39:02 ???
URL: http://web.eecs.umich.edu/~shiyy/mypapers/DLS08.pdf
acknowledgement: ack-nhfb
rawauthor: Ye Du and James Leung and Yaoyun Shi
rawtitle: {PerturbationRank}: A Non-monotone Ranking Algorithm
entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 0769534902
ISBN13: 978-0-7695-3490-9 OR 9780769534909
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4756692
keywords: pagerank algorithm; software measurement; unified modeling language
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4756503
rawauthor: Fagui Li and Tong Yi
rawbooktitle: {PACIIA '08: Pacific-Asia Workshop on Computational Intelligence and Industrial Application (2008)}
raweditor: {IEEE}
rawtitle: Apply {PageRank} Algorithm to Measuring Relationship's Complexity
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: Lin:2008:PHR
author: Jimmy Lin
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 OR 14712105
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
bibtimestamp: 2011.06.03 10:03:23 MDT
URL: http://www.biomedcentral.com/1471-2105/9/270; http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2442104/
fjournal: BMC Bioinformatics
keywords: BioMed Central (BMC)
acknowledgement: ack-nhfb
ajournal: BMC Bioinf.
journalURL: http://www.biomedcentral.com/bmcbioinformatics/
rawauthor: Jimmy Lin
title:
PageRank without hyperlinks: Reranking with PubMed related article networks for biomedical text retrieval

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.

rawabstract:
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.

rawtitle:
{PageRank} without hyperlinks: Reranking with {PubMed} related article networks for biomedical text retrieval

entry:
@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)",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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) OR 03029743 (print), 16113349 (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
bibtimestamp: 2010.05.05 19:28:06 ???
series: ser-LNCS
ZMnumber: 1142.68314
acknowledgement: ack-nhfb
rawauthor: Nelly Litvak and Werner R. W. Scheinhardt and Yana Volkovich
rawtitle: Probabilistic relation between in-degree and {PageRank}
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.

keywords:
Algorithms; Experimentation; In-Degree; PageRank; Power law; Regular variation; Stochastic equation; Theory; Verification; Web measurement

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
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 OR 160558164X
ISBN13: 978-1-60558-164-4 OR 9781605581644
LCCN: QA76.9.D3
bibdate: Tue Aug 11 17:22:35 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:22:35 ???
URL: http://dl.acm.org/citation.cfm?id=1390334
acknowledgement: ack-nhfb
bookURL: http://www.sigir2008.org/papers.html
bookpages: xxviii + 906
rawauthor: Y. Liu and B. Gao and T.-Y. Liu and Y. Zhang and Z. Ma and S. He and H. Li
rawtitle: {BrowseRank}: Letting web users vote for page importance
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

rawbooktitle:
{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}

raweditor:
Sung Hyon Myaeng and Douglas W. Oard and Fabrizio Sebastiani and T. S. (Tat-Seng) Chua and Mun-Kew Leong

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 0769533167
ISBN13: 978-0-7695-3316-2 OR 9780769533162
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4570834
keywords: Personalized PageRank; Naive Bayes model; personalized prior probability
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4570797
rawauthor: Yong Liu and Xiaolei Wang and Jin Zhang and Hongbo Xu
rawbooktitle: {WSCS '08: IEEE International Workshop on Semantic Computing and Systems (2008)}
raweditor: {IEEE}
rawtitle: Personalized {PageRank} Based Multi-document Summarization
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 Naive 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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 3
year: 2008
DOI: https://doi.org/10.1145/324133.324140
bibdate: Sat May 8 18:33:04 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:04 MDT
keywords: citation analysis; citation network; internal citations; PageRank
acknowledgement: ack-nhfb
rawauthor: Nan Ma and Jiancheng Guan and Yi Zhao
rawtitle: Bringing {PageRank} to the citation analysis
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.

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: McGettrick:2008:FAP
author: S. McGettrick and D. Geraghty and C. McElroy
editor: Udo Kebschull and Marco Platzner and Jurgen Teich
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) OR 1424419611, 1424419603 (set)
ISBN13: 978-1-4244-1961-6, 978-1-4244-1960-9 (set) OR 9781424419616, 9781424419609 (set)
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
note: IEEE catalog number CFP08623.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4629999
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4625340
rawauthor: S. McGettrick and D. Geraghty and C. McElroy
raweditor: Udo Kebschull and Marco Platzner and J{\"u}rgen Teich
rawnote: IEEE catalog number CFP08623.
rawtitle: An {FPGA} architecture for the {PageRank} eigenvector problem
booktitle:
FPL 2008: International Conference on Field-Programmable Logic and Applications: Heidelberg, Germany, September 8--10, 2008

rawbooktitle:
{FPL 2008: International Conference on Field-Programmable Logic and Applications: Heidelberg, Germany, September 8--10, 2008}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: McGettrick:2008:TFS
author: Seamas McGettrick and Dermot Geraghty and Ciaran McElroy
editor: Christian Bischof and others
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 OR 158603796X
ISBN13: 978-1-58603-796-3 OR 9781586037963
MRclass: 68M10 65F30 65Y10 65Y20
bibdate: Thu May 06 11:31:36 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 11:31:36 ???
series: Advances in Parallel Computing
ZMnumber: 1160.68317
acknowledgement: ack-nhfb
rawauthor: S{\'e}amas McGettrick and Dermot Geraghty and Ciar{\'a}n McElroy
raweditor: Christian Bischof and others
rawtitle: Towards an {FPGA} solver for the {PageRank} eigenvector problem
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

rawbooktitle:
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

entry:
@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,
}


bibtype: article
filename: pagerank.bib
label: 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
MRclass: 68M11 68M10 68P10
bibdate: Thu May 06 11:29:26 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 11:29:26 ???
language: Chinese
ZMnumber: 1180.68039
acknowledgement: ack-nhfb
rawauthor: Hao Pan and Long-Yuan Tan
rawtitle: Adaptive {PageRank} algorithm search strategy for specific topics
entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: Parreira:2008:JAP
journal: j-VLDB-J
volume: 17
number: 2
pages: 291--313
month: mar
monthnumber: 3
year: 2008
CODEN: VLDBFR
DOI: https://doi.org/10.1007/s00778-007-0057-y
ISSN: 1066-8888 (print), 0949-877X (electronic) OR 10668888 (print), 0949877X (electronic)
ISSNL: 1066-8888 OR 10668888
bibdate: Sat May 8 18:33:08 MDT 2010
bibtimestamp: 2010.05.08 18:33:08 MDT
fjournal: VLDB Journal: Very Large Data Bases
keywords: link analysis; Markov chain aggregation; peer-to-peer systems; social reputation; Web graph
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/toc.cfm?id=J869
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

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.

rawabstract:
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.

rawauthor:
Josiane Xavier Parreira and Carlos Castillo and Debora Donato and Sebastian Michel and Gerhard Weikum

rawtitle:
The {Juxtaposed} approximate {PageRank} method for robust {PageRank} approximation in a peer-to-peer web search network

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 1424418364
ISBN13: 978-1-4244-1836-7 OR 9781424418367
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4497599
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4492792
rawauthor: Amit Pathak and Soumen Chakrabarti and Manish Gupta
rawbooktitle: {ICDE 2008: IEEE 24th International Conference on Data Engineering}
raweditor: {IEEE}
rawtitle: Index Design for Dynamic Personalized {PageRank}
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%.

rawabstract:
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\%.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2008.06.20 14:17:29 MDT
URL: https://www.math.utah.edu/pub/tex/bib/pods.bib
keywords: graph conductance; mixing time; PageRank; random walk; streaming algorithms
acknowledgement: ack-nhfb
rawauthor: Atish Das Sarma and Sreenivas Gollapudi and Rina Panigrahy
rawtitle: Estimating {PageRank} on graph streams
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: Sidi:2008:VEM
author: Avram Sidi
journal: j-COMPUT-MATH-APPL
volume: 56
number: 1
pages: 1--24
month: jul
monthnumber: 7
year: 2008
CODEN: CMAPDK
DOI: https://doi.org/10.1016/j.camwa.2007.11.027
ISSN: 0898-1221 (print), 1873-7668 (electronic) OR 08981221 (print), 18737668 (electronic)
ISSNL: 0898-1221 OR 08981221
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
bibtimestamp: 2010.05.08 18:33:11 MDT
fjournal: Computers & Mathematics with Applications. An International Journal
ZMnumber: 1145.65312
acknowledgement: ack-nhfb
rawauthor: Avram Sidi
title:
Vector extrapolation methods with applications to solution of large systems of equations and to PageRank computations

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.

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

rawabstract:
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.

rawtitle:
Vector extrapolation methods with applications to solution of large systems of equations and to {PageRank} computations

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 2
year: 2008
CODEN: POLNCL
DOI: https://doi.org/10.1371/journal.pone.0001683
ISSN: 1932-6203 OR 19326203
bibdate: Fri Mar 11 16:17:22 2016
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2016.03.11 16:17:22 ???
URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0001683
fjournal: PLoS One
acknowledgement: ack-nhfb
journalURL: http://www.plosone.org/
rawauthor: M. J. Stringer and M. Sales-Pardo and L. S. {Nunes Amaral}
rawtitle: Effectiveness of journal ranking schemes as a tool for locating information
entry:
@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/",
}


bibtype: inproceedings
filename: pagerank.bib
label: Su:2008:ERR
author: Ja-Hwung Su and Bo-Wen Wang and Vincent S. Tseng
editor: IEEE
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 OR 0769534961
ISBN13: 978-0-7695-3496-1 OR 9780769534961
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4740820
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4740404
rawauthor: Ja-Hwung Su and Bo-Wen Wang and Vincent S. Tseng
raweditor: {IEEE}
booktitle:
WI-IAT '08: IEEE / WIC / 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

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.

rawabstract:
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.

rawbooktitle:
{WI-IAT '08: IEEE\slash WIC\slash ACM International Conference on Web Intelligence and Intelligent Agent Technology (2008)}

rawtitle:
Effective Ranking and Recommendation on {Web} Page Retrieval by Integrating Association Mining and {PageRank}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Tripathy:2008:WMA
author: Animesh Tripathy and Prashanta K. Patra
editor: IEEE
booktitle: APSCC '08: IEEE Asia-Pacific Services Computing Conference (2008)
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 OR 0769534732
ISBN13: 978-0-7695-3473-2 OR 9780769534732
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4780726
keywords: Crawler; Data Mining; PageRank; Web Mining
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4780614
rawauthor: Animesh Tripathy and Prashanta K. Patra
rawbooktitle: {APSCC '08: IEEE Asia-Pacific Services Computing Conference (2008)}
raweditor: {IEEE}
title:
A Web Mining Architectural Model of Distributed Crawler for Internet Searches Using PageRank Algorithm

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.

rawabstract:
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.

rawtitle:
A {Web} Mining Architectural Model of Distributed Crawler for {Internet} Searches Using {PageRank} Algorithm

entry:
@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",
}


bibtype: mastersthesis
filename: pagerank.bib
label: Tudisco:2008:MAN
author: F. Tudisco
type: Bachelor thesis
school: Dipartimento di Matematica, Universita degli studi di Roma ``Tor Vergata''
address: Rome, Italy
pages: 513--518
year: 2008
bibdate: Wed Nov 30 08:15:21 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.11.30 08:15:21 ???
language: Italian
acknowledgement: ack-nhfb
rawauthor: 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]

rawtitle:
Metodi analitico numerici per il problema del ranking delle pagine web. ({Italian}) [{Numerical} analytic method for the problem of ranking {Web} pages]

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 3
year: 2008
CODEN: ATISET
DOI: https://doi.org/10.1145/1344411.1344416
ISSN: 1046-8188 OR 10468188
ISSNL: 0734-2047 OR 07342047
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
bibtimestamp: 2008.06.12 16:52:34 MDT
URL: https://www.math.utah.edu/pub/tex/bib/tois.bib
fjournal: ACM Transactions on Information Systems
keywords: DirichletRank; link analysis; PageRank; spamming; zero-one gap
acknowledgement: ack-nhfb
articleno: 10
rawauthor: Xuanhui Wang and Tao Tao and Jian-Tao Sun and Azadeh Shakery and Chengxiang Zhai
rawtitle: {DirichletRank}: {Solving} the zero-one gap problem of {PageRank}
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.

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4525246
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4489617
rawauthor: Jinghua Wang and Jianyi Liu and Cong Wang and Ping Zhang
rawbooktitle: {ICNSC 2008: IEEE International Conference on Networking, Sensing and Control}
rawtitle: Keyword Indexing System with {HowNet} and {PageRank}
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Wang:2008:RDR
author: Jue Wang and Jian Peng and Daping Zhang
editor: IEEE
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 OR 0769533361
ISBN13: 978-0-7695-3336-0 OR 9780769533360
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4722467
keywords: feedback; PageRank; reputation; trust
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721667
rawauthor: Jue Wang and Jian Peng and Daping Zhang
raweditor: {IEEE}
rawtitle: Research on Dynamic Reputation Management Model Based on {PageRank}
booktitle:
CSSE '08: Proceedings of the 2008 International Conference on Computer Science and Software Engineering

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.

rawabstract:
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.

rawbooktitle:
CSSE '08: Proceedings of the 2008 International Conference on Computer Science and Software Engineering

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: Wu:2008:CJC
author: Gang Wu and Yimin Wei
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) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
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
bibtimestamp: 2010.05.05 19:28:06 ???
note: See cite Serra-Capizzano:2005:JCF.
fjournal: SIAM Journal on Matrix Analysis and Applications
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Gang Wu and Yimin Wei
rawnote: See \cite{Serra-Capizzano:2005:JCF}.
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

rawtitle:
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}

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: Wu:2008:EJC
author: Gang Wu
journal: j-J-COMPUT-APPL-MATH
volume: 216
number: 2
pages: 364--370
month: jun
monthnumber: 6
year: 2008
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2007.05.015
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
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
bibtimestamp: 2010.05.08 18:33:10 MDT
fjournal: Journal of Computational and Applied Mathematics
ZMnumber: 1148.15007
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Gang Wu
title:
Eigenvalues and Jordan canonical form of a successively rank-one updated complex matrix with applications to Google's PageRank problem

keywords:
65C40; 65F10; 65F15; Generalized Google matrix; Google matrix; Jordan canonical form; Pagerank; Successively rank-one updated matrix

rawtitle:
Eigenvalues and {Jordan} canonical form of a successively rank-one updated complex matrix with applications to {Google}'s {PageRank} problem

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Yang:2008:APT
author: Shenggang Yang and Jianmin Zhao and Xueyan Zhang and Limei Zhao
editor: Elvis Wai Chung Leung and others
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 OR 3540899626
ISBN13: 978-3-540-89962-4 OR 9783540899624
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
series: ser-LNCS
keywords: Collaborative/cooperative learning; fairness gene; knowledge acquiring; PageRank
acknowledgement: ack-nhfb
rawauthor: Shenggang Yang and Jianmin Zhao and Xueyan Zhang and Limei Zhao
raweditor: Elvis Wai Chung Leung and others
rawtitle: Application of {PageRank} Technique in Collaborative Learning
booktitle:
Advances in Blended Learning: Second Workshop on Blended Learning, WBL 2008, Jinhua, China, August 20--22, 2008. Revised Selected Papers

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.

rawabstract:
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.

rawbooktitle:
{Advances in Blended Learning: Second Workshop on Blended Learning, WBL 2008, Jinhua, China, August 20--22, 2008. Revised Selected Papers}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Zhang:2008:NRA
author: Liyan Zhang and Chunping Li
editor: Wayne Wobcke and Mengjie Zhang
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 OR 3540893776
ISBN13: 978-3-540-89377-6 OR 9783540893776
LCCN: Q334 .A97 2008
bibdate: Sat May 8 18:33:07 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:07 MDT
series: ser-LNAI
acknowledgement: ack-nhfb
rawauthor: Liyan Zhang and Chunping Li
raweditor: Wayne Wobcke and Mengjie Zhang
rawtitle: A Novel Recommending Algorithm Based on Topical {PageRank}
booktitle:
Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence

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.

rawabstract:
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.

rawbooktitle:
Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 0769533051
ISBN13: 978-0-7695-3305-6 OR 9780769533056
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4666388
keywords: A-PageRank; anchor text; PageRank; PFT; VSM
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4665920
rawauthor: Yong Zhang and Long-bin Xiao and Bin Fan
rawbooktitle: {FSKD '08: Fifth International Conference on Fuzzy Systems and Knowledge Discovery (2008)}
rawtitle: The Research about {Web} Page Ranking Based on the {A-PageRank} and the {Extended VSM}
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.

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Zhang:2008:TPB
author: Liyan Zhang and Kai Zhang and Chunping Li
editor: ACM
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 OR 160558164X
ISBN13: 978-1-60558-164-4 OR 9781605581644
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
keywords: recommender system; topical PageRank
acknowledgement: ack-nhfb
rawauthor: Liyan Zhang and Kai Zhang and Chunping Li
raweditor: {ACM}
rawtitle: A topical {PageRank} based algorithm for recommender systems
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

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.

rawabstract:
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.

rawbooktitle:
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

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Agirre:2009:PPW
author: Eneko Agirre and Aitor Soroa
editor: Alex Lascarides and Claire Gardent and Joakim Nivre
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 OR 1932432167
ISBN13: 978-1-932432-16-9 OR 9781932432169
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
acknowledgement: ack-nhfb
rawauthor: Eneko Agirre and Aitor Soroa
raweditor: Alex Lascarides and Claire Gardent and Joakim Nivre
rawtitle: Personalizing {PageRank} for word sense disambiguation
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

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.

rawabstract:
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.

rawbooktitle:
{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}

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 3642008860
ISBN13: 978-3-642-00886-3 OR 9783642008863
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (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
bibtimestamp: 2010.05.08 18:33:10 MDT
series: ser-LNCS
acknowledgement: ack-nhfb
bookpages: xix + 797
rawauthor: Md. Hijbul Alam and Jongwoo Ha and Sangkeun Lee
rawbooktitle: Proceedings of the 14th International Conference on Database Systems for Advanced Applications
raweditor: Xiaofang Zhou and others
rawtitle: Fractional {PageRank} Crawler: Prioritizing {URLs} Efficiently for Crawling Important Pages Early
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 1
year: 2009
DOI: https://doi.org/10.1145/1462148.1462151
ISSN: 1559-1131 (print), 1559-114X (electronic) OR 15591131 (print), 1559114X (electronic)
ISSNL: 1559-1131 OR 15591131
bibdate: Fri Apr 24 18:18:15 MDT 2009
bibsource: http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2009.04.24 18:18:15 MDT
URL: https://www.math.utah.edu/pub/tex/bib/tweb.bib
fjournal: ACM Transactions on the Web (TWEB)
keywords: antialiasing; crawling; duplicate detection; Search engines; URL normalization
acknowledgement: ack-nhfb
articleno: 3
rawauthor: Ziv Bar-Yossef and Idit Keidar and Uri Schonfeld
rawtitle: Do not crawl in the {DUST}: {Different URLs with Similar Text}
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 11
year: 2009
CODEN: ATISET
DOI: https://doi.org/10.1145/1062745.1062826
ISSN: 1046-8188 OR 10468188
ISSNL: 0734-2047 OR 07342047
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
bibtimestamp: 2010.03.15 12:37:02 MDT
fjournal: ACM Transactions on Information Systems
keywords: damping factor; PageRank; power method
acknowledgement: ack-nhfb
articleno: 19
rawauthor: Paolo Boldi and Massimo Santini and Sebastiano Vigna
rawtitle: {PageRank}: {Functional} dependencies
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Chen:2009:IPA
author: Xiaoyun Chen and Baojun Gao and Ping Wen
editor: Xin Li and Wenbin Hu and others
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 OR 1424449944
ISBN13: 978-1-4244-4994-1 OR 9781424449941
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
note: IEEE catalog number CFP0990H.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5364637
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5362513
rawauthor: Xiaoyun Chen and Baojun Gao and Ping Wen
raweditor: Xin Li and Wenbin Hu and others
rawnote: IEEE catalog number CFP0990H.
rawtitle: An Improved {PageRank} Algorithm Based on Latent Semantic Model
booktitle:
Proceedings, 2009 International Conference on Information Engineering and Computer Science: ICIECS 2009, Wuhan China 19--20 December 2009

rawbooktitle:
{Proceedings, 2009 International Conference on Information Engineering and Computer Science: ICIECS 2009, Wuhan China 19--20 December 2009}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 3642022693
ISBN13: 978-3-642-02269-2 OR 9783642022692
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
MRclass: 68Wxx
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
series: ser-LNCS
ZMnumber: 05578464
acknowledgement: ack-nhfb
rawauthor: Wei Chen and Shang-Hua Teng and Yajun Wang and Yuan Zhou
rawtitle: On the $ \alpha $-sensitivity of {Nash} equilibria in {PageRank}-based network reputation games
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 $.

rawabstract:
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 $.

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 3540959947
ISBN13: 978-3-540-95994-6 OR 9783540959946
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
MRclass: 68M10
bibdate: Sat May 8 18:33:04 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:04 MDT
series: ser-LNCS
ZMnumber: 05505865
acknowledgement: ack-nhfb
rawauthor: Fan Chung
rawtitle: A Local Graph Partitioning Algorithm Using Heat Kernel {PageRank}
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| $.

rawabstract:
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|} $.

entry:
@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,
}


bibtype: misc
filename: pagerank.bib
label: Cutts:2009:PS
author: Matt Cutts
title: PageRank sculpting
pages: 62--75
year: 2009
bibdate: Tue Aug 11 16:35:56 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:35:56 ???
URL: http://www.mattcutts.com/blog/pagerank-sculpting/
acknowledgement: ack-nhfb
howpublished: Gadgets, Google, and SEO blog.
rawauthor: Matt Cutts
rawtitle: {PageRank} sculpting
entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 0769537359
ISBN13: 978-0-7695-3735-1 OR 9780769537351
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5360078
keywords: Hyperlink Analysis; Information Retrieval; PageRank
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5358480
rawauthor: Kaiying Deng and Tieli Sun and Jingwei Deng
rawbooktitle: {FSKD '09: Sixth International Conference on Fuzzy Systems and Knowledge Discovery (2009)}
raweditor: {IEEE}
rawtitle: The General Extrapolation Formula for Acceleration {PageRank} Computations
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 11
year: 2009
CODEN: JASIEF
DOI: https://doi.org/10.1145/1013367.1013519
ISSN: 1532-2882 (print), 1532-2890 (electronic) OR 15322882 (print), 15322890 (electronic)
bibdate: Sat May 8 18:33:08 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:08 MDT
fjournal: Journal of the American Society for Information Science and Technology
keywords: authors; citation analysis; co-citation networks; ranking; weighting
acknowledgement: ack-nhfb
ajournal: J. Am. Soc. Inf. Sci. Technol.
rawauthor: Ying Ding and Erjia Yan and Arthur Frazho and James Caverlee
rawtitle: {PageRank} for ranking authors in co-citation networks
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.

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5192339
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5174536
rawauthor: Lianxiong Gao and Jianping Wu and Liu Rui
rawbooktitle: {CCDC '09: Chinese Control and Decision Conference (2009)}
rawtitle: Key nodes mining in transport networks based in {PageRank} algorithm
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Imran:2009:ERP
author: Naveed Imran and Jingen Liu and Jiebo Luo and Mubarak Shah
editor: ACM
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 OR 1605586080
ISBN13: 978-1-60558-608-3 OR 9781605586083
bibdate: Sat May 8 18:33:08 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:08 MDT
keywords: CBIR; event category recognition; pagerank
acknowledgement: ack-nhfb
rawauthor: Naveed Imran and Jingen Liu and Jiebo Luo and Mubarak Shah
raweditor: {ACM}
rawtitle: Event recognition from photo collections via {PageRank}
booktitle:
International Multimedia Conference Proceedings of the seventeen ACM international conference on Multimedia

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.

rawabstract:
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.

rawbooktitle:
International Multimedia Conference Proceedings of the seventeen ACM international conference on Multimedia

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5160351
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5089257
rawauthor: Hideaki Ishii and Roberto Tempo
rawbooktitle: {ACC '09: American Control Conference (2009)}
raweditor: {IEEE}
rawtitle: Distributed {PageRank} computation with link failures
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.

keywords:
distributed computation; link failures; multi-agent consensus; pagerank algorithm; randomization; stochastic matrices

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Ishii:2009:DRP
author: H. Ishii and R. Tempo and Er-Wei Bai and F. Dabbene
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
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5399514
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695
rawauthor: H. Ishii and R. Tempo and Er-Wei Bai and F. Dabbene
rawtitle: Distributed randomized {PageRank} computation based on web aggregation
booktitle:
CDC / CCC 2009: Proceedings of the 48th IEEE Conference on Decision and Control [held jointly with the 2009 28th Chinese Control Conference]

rawbooktitle:
{CDC\slash CCC 2009: Proceedings of the 48th IEEE Conference on Decision and Control [held jointly with the 2009 28th Chinese Control Conference]}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Ishii:2009:FLS
author: H. Ishii and R. Tempo
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
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5399501
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695
rawauthor: H. Ishii and R. Tempo
rawtitle: Fragile link structure in {PageRank} computation
booktitle:
CDC / CCC 2009: Proceedings of the 48th IEEE Conference on Decision and Control [held jointly with the 2009 28th Chinese Control Conference]

rawbooktitle:
{CDC\slash CCC 2009: Proceedings of the 48th IEEE Conference on Decision and Control [held jointly with the 2009 28th Chinese Control Conference]}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Jager:2009:PSH
author: Douglas V. Jager and Jeremy T. Bradley
editor: Leif Azzopardi and others
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 OR 3642044166
ISBN13: 978-3-642-04416-8 OR 9783642044168
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (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
bibtimestamp: 2010.05.08 18:33:09 MDT
series: ser-LNCS
acknowledgement: ack-nhfb
rawauthor: Douglas V. Jager and Jeremy T. Bradley
raweditor: Leif Azzopardi and others
rawtitle: {PageRank}: Splitting Homogeneous Singular Linear Systems of Index One
booktitle:
Advances in information retrieval theory: second International Conference on the Theory of Information Retrieval, ICTIR 2009, Cambridge, UK, September 10--12, 2009: proceedings

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.

rawabstract:
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.

rawbooktitle:
{Advances in information retrieval theory: second International Conference on the Theory of Information Retrieval, ICTIR 2009, Cambridge, UK, September 10--12, 2009: proceedings}

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 076953726X
ISBN13: 978-0-7695-3726-9 OR 9780769537269
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5254238
keywords: concern impact analysis; concern relationship graph; PageRank algorithm
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5254044
rawauthor: Ying Jin and Jing Zhang and Pengfei Ma and Weiping Hao and Shutong Luo and Zepeng Li
rawbooktitle: {COMPSAC '09: 33rd Annual IEEE International Computer Software and Applications Conference, 2009}
raweditor: {IEEE}
rawtitle: Applying {PageRank} Algorithm in Requirement Concern Impact Analysis
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 8
year: 2009
CODEN: CACMA2
DOI: https://doi.org/10.1145/1536616.1536649
ISSN: 0001-0782 (print), 1557-7317 (electronic) OR 00010782 (print), 15577317 (electronic)
ISSNL: 0001-0782 OR 00010782
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
bibtimestamp: 2009.09.02 16:54:35 MDT
URL: https://www.math.utah.edu/pub/tex/bib/cacm2000.bib
fjournal: Communications of the ACM
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J79
rawauthor: Rohit Kaul and Yeogirl Yun and Seong-Gon Kim
rawtitle: Ranking billions of {Web} pages using diodes
abstract:
Introductionpar 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.

rawabstract:
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.

entry:
@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",
}


bibtype: techreport
filename: pagerank.bib
label: 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
monthnumber: 10
year: 2009
bibdate: Tue Aug 11 17:14:02 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:14:02 ???
URL: http://www.ca.sandia.gov/~tgkolda/pubs/bibtgkfiles/SAND2009-6670%20BadRank.pdf
acknowledgement: ack-nhfb
rawauthor: Tamara G. Kolda and Michel J. Procopio
rawtitle: Generalized {BadRank} with Graduated Trust
entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 1424427223
ISBN13: 978-1-4244-2722-2 OR 9781424427222
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
keywords: complex network; key nodes mining; pagerank algorithm; transport network
acknowledgement: ack-nhfb
rawauthor: Gao Lianxiong and Wu Jianping and Liu Rui
rawbooktitle: Proceedings of the 21st annual international conference on Chinese control and decision conference
raweditor: {IEEE}
rawtitle: Key nodes mining in transport networks based on {PageRank} algorithm
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.

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 2
year: 2009
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2008.06.003
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
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
bibtimestamp: 2010.05.08 18:33:09 MDT
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Yiqin Lin and Xinghua Shi and Yimin Wei
rawtitle: On computing {PageRank} via lumping the {Google} matrix
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.

keywords:
65B99; 65F10; 65F15; 65F50; Dangling node; Google matrix; Lumping; PageRank; Power method; Weakly nondangling node

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Ling:2009:IPW
author: Zhang Ling and Qin Zheng
editor: IEEE
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 OR 0769538878
ISBN13: 978-0-7695-3887-7 OR 9780769538877
bibdate: Sat May 8 18:33:04 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:04 MDT
acknowledgement: ack-nhfb
rawauthor: Zhang Ling and Qin Zheng
raweditor: {IEEE}
rawtitle: The Improved {PageRank} in {Web} Crawler
booktitle:
ICISE Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering

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.

rawabstract:
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.

rawbooktitle:
ICISE Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 3540959947
ISBN13: 978-3-540-95994-6 OR 9783540959946
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
series: ser-LNCS
keywords: Multivariate extremes; PageRank; Power law graphs; Regular variation
acknowledgement: ack-nhfb
rawauthor: Nelly Litvak and Werner Scheinhardt and Yana Volkovich and Bert Zwart
rawtitle: Characterization of Tail Dependence for In-Degree and {PageRank}
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.

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 1424449944
ISBN13: 978-1-4244-4994-1 OR 9781424449941
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5364487
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5362513
rawauthor: Yaqing Liu and Rong Chen and Hong Yang
rawbooktitle: {ICIECS 2009: International Conference on Information Engineering and Computer Science}
rawtitle: Entity-Relation Extraction for {Chinese} Based on Pattern Evolution and {PageRank}
entry:
@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",
}


bibtype: book
filename: pagerank.bib
label: Lowe:2009:GSS
author: Janet Lowe
publisher: pub-WILEY
address: pub-WILEY:adr
pages: xiii + 315
year: 2009
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
bibtimestamp: 2011.06.03 09:52:48 MDT
acknowledgement: ack-nhfb
subjectdates: 1973--; 1973--
rawauthor: Janet Lowe
title:
Google speaks: secrets of the world's greatest billionaire entrepreneurs, Sergey Brin and Larry Page

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) OR 0470501227 (ebook), 0470501243 (ebook: Adobe Digital Editions), 0470501235 (ebook: Mobipocket Reader), 047039854X (cloth)

ISBN13:
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) OR 9780470501221 (ebook), 9780470501245 (ebook: Adobe Digital Editions), 9780470501238 (ebook: Mobipocket Reader), 9780470398548 (cloth)

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.

subject:
Brin, Sergey; Page, Larry; Computer programmers; United States; Biography; Businesspeople; Internet programming; Google; Web search engines

TOC:
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

rawabstract:
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.

rawtitle:
{Google} speaks: secrets of the world's greatest billionaire entrepreneurs, {Sergey Brin} and {Larry Page}

rawTOC:
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

entry:
@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:
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                 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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Mataoui:2009:EPA
author: M. Mataoui and M. Boughanem and M. Mezghiche
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 OR 142444456X
ISBN13: 978-1-4244-4456-4 OR 9781424444564
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5273944
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5254891
rawauthor: M. Mataoui and M. Boughanem and M. Mezghiche
rawtitle: Experiments on {PageRank} algorithm in the {XML} information retrieval context
booktitle:
ICADIWT '09: Second International Conference on the Applications of Digital Information and Web Technologies (2009)

rawbooktitle:
{ICADIWT '09: Second International Conference on the Applications of Digital Information and Web Technologies (2009)}

entry:
@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",
}


bibtype: misc
filename: pagerank.bib
label: Meng:2009:CBS
author: X. Meng
title: Computing BookRank via Social Cataloging
pages: 33
day: 22
month: feb
monthnumber: 2
year: 2009
bibdate: Tue Aug 11 17:25:15 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:25:15 ???
URL: http://cads.stanford.edu/projects/presentations/2009visit/bookrank.pdf
acknowledgement: ack-nhfb
howpublished: Web slides for CADS 2010 conference.
rawauthor: X. Meng
rawtitle: Computing {BookRank} via Social Cataloging
entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: Nazin:2009:ARA
author: A. Nazin and B. Polyak
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 127--132
year: 2009
DOI: https://doi.org/10.1109/CDC.2009.5400036
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5400036
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695
rawauthor: A. Nazin and B. Polyak
booktitle:
CDC / 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

rawbooktitle:
{CDC\slash CCC 2009: Proceedings of the 48th IEEE Conference on Decision and Control [held jointly with the 2009 28th Chinese Control Conference]}

rawtitle:
Adaptive randomized algorithm for finding eigenvector of stochastic matrix with application to {PageRank}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Nazin:2009:RAFa
author: A. Nazin and B. Polyak
booktitle: ISIC 2009: IEEE Control Applications, (CCA) & Intelligent Control
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 412--416
year: 2009
DOI: https://doi.org/10.1109/CCA.2009.5280707
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5280707
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5268173
rawauthor: A. Nazin and B. Polyak
rawbooktitle: {ISIC 2009: IEEE Control Applications, (CCA) \& Intelligent Control}
title:
A randomized algorithm for finding eigenvector of stochastic matrix with application to PageRank problem

rawtitle:
A randomized algorithm for finding eigenvector of stochastic matrix with application to {PageRank} problem

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: Nazin:2009:RAFb
author: A. V. Nazin and B. T. Polyak
journal: j-DOKL-AKAD-NAUK
volume: 426
number: 6
pages: 734--737
year: 2009
CODEN: DANKAS
ISSN: 0869-5652 OR 08695652
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
bibtimestamp: 2010.05.05 19:28:06 ???
note: English translation in Dokl. Math. 79(3) 424--427 (2009).
fjournal: Rossiui skaya Akademiya Nauk. Doklady Akademii Nauk
acknowledgement: ack-nhfb
rawauthor: A. V. Nazin and B. T. Polyak
rawnote: English translation in Dokl. Math. 79(3) 424--427 (2009).
title:
A randomized algorithm for finding an eigenvector of a stochastic matrix with application to PageRank

rawtitle:
A randomized algorithm for finding an eigenvector of a stochastic matrix with application to {PageRank}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Phuoc:2009:PVK
author: Nguyen Quang Phuoc and Sung-Ryul Kim and Han-Ku Lee and Hyung Seok Kim
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 OR 0769538967
ISBN13: 978-0-7695-3896-9 OR 9780769538969
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5368419
keywords: Katz status index; RageRank; search engines; World Wide Web
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5367867
rawauthor: Nguyen Quang Phuoc and Sung-Ryul Kim and Han-Ku Lee and Hyung Seok Kim
rawtitle: {PageRank} vs. {Katz Status Index}, a Theoretical Approach
booktitle:
ICCIT '09: Fourth International Conference on Computer Sciences and Convergence Information Technology (2009)

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.

rawabstract:
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.

rawbooktitle:
{ICCIT '09: Fourth International Conference on Computer Sciences and Convergence Information Technology (2009)}

entry:
@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",
}


bibtype: incollection
filename: pagerank.bib
label: 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
bibtimestamp: 2010.07.20 16:43:36 ???
language: French
acknowledgement: ack-nhfb
rawauthor: Christiane Rousseau and Yvan Saint-Aubin
rawtitle: {Google} et l'algorithme {PageRank}
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5170706
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5170260
rawauthor: Cheng Su and YunTao Pan and JunPeng Yuan and Hong Guo and ZhengLu Yu and ZhiYu Hu
rawbooktitle: {2009 WRI World Congress on Computer Science and Information Engineering}
rawtitle: {PageRank}, {HITS} and Impact Factor for Journal Ranking
entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 12
year: 2009
bibdate: Tue Aug 11 17:40:40 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:40:40 ???
URL: http://arxiv.org/abs/0912.0238
acknowledgement: ack-nhfb
rawauthor: Sebastiano Vigna
rawtitle: Spectral Ranking
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.

rawabstract:
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.

entry:
@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,
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5255060
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5234367
rawauthor: Jing Wan and Si-Xue Bai
rawbooktitle: {GRC '09: IEEE International Conference on Granular Computing (2009)}
rawtitle: An improvement of {PageRank} algorithm based on the time-activity-curve
entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
year: 2009
CODEN: SJMAEL
DOI: https://doi.org/10.1137/070698129
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
MRclass: 62F07 (65F15 68P20)
MRnumber: 2486859 (2010d:62041)
MRreviewer: Truc Nguyen
bibdate: Tue May 18 22:32:31 MDT 2010
bibtimestamp: 2010.05.18 22:32:31 MDT
fjournal: SIAM Journal on Matrix Analysis and Applications
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Rebecca S. Wills and Ilse C. F. Ipsen
rawtitle: Ordinal Ranking for {Google}'s {PageRank}
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

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 5
year: 2009
CODEN: CMAPDK
DOI: https://doi.org/10.1016/j.camwa.2009.02.027
ISSN: 0898-1221 (print), 1873-7668 (electronic) OR 08981221 (print), 18737668 (electronic)
ISSNL: 0898-1221 OR 08981221
bibdate: Thu Dec 29 08:16:04 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.12.29 08:16:04 ???
URL: http://www.sciencedirect.com/science/article/pii/S0898122109002247
fjournal: Computers and Mathematics with Applications
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: Zhiping Xiong and Bing Zheng
rawtitle: On the eigenvalues of a specially rank-$r$ updated complex matrix
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Yen:2009:API
author: Chia-Chen Yen and Jih-Shih Hsu
editor: IEEE
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 OR 142443808X
ISBN13: 978-1-4244-3808-2 OR 9781424438082
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5068909
keywords: document relevance; information retrieval; pagerank; topic-sensitive; web search
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5038837
rawauthor: Chia-Chen Yen and Jih-Shih Hsu
raweditor: {IEEE}
rawtitle: Associated {PageRank}: Improved {PageRank} measured by frequent term sets
booktitle:
VECIMS '09: IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems (2009), May 11--13, 2009, Hong Kong, China

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.

rawabstract:
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.

rawbooktitle:
{VECIMS '09: IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems (2009), May 11--13, 2009, Hong Kong, China}

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5277414
keywords: document relevance; information retrieval; pagerank; topic-sensitive; Web search
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5247842
rawauthor: Chia-Chen Yen and Jih-Shih Hsu
rawbooktitle: {FUZZ-IEEE 2009: IEEE International Conference on Fuzzy Systems}
rawtitle: {PageRank} algorithm improvement by page relevance measurement
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.

rawabstract:
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.

entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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 OR 142444909X
ISBN13: 978-1-4244-4909-5 OR 9781424449095
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5455065
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5454173
rawauthor: Ling Zhang and Zheng Qin
rawbooktitle: {2009 1st International Conference on Information Science and Engineering (ICISE)}
rawtitle: The Improved {Pagerank} in {Web} Crawler
entry:
@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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Zheng:2009:LSP
author: Ling Zheng and Ning Zhang and Yang Bo
editor: IEEE
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 OR 0769538878
ISBN13: 978-0-7695-3887-7 OR 9780769538877
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5455348
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5454173
rawauthor: Ling Zheng and Ning Zhang and Yang Bo
raweditor: {IEEE}
rawtitle: Link-Sensitive {PageRank}: An Improved Ranking Algorithm for Vertical Search Engines
booktitle:
ICISE '09: Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering

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.

rawabstract:
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.

rawbooktitle:
{ICISE '09: Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering}

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 4
year: 2010
CODEN: JACOAH
DOI: https://doi.org/10.1145/1734213.1734220
ISSN: 0004-5411 (print), 1557-735X (electronic) OR 00045411 (print), 1557735X (electronic)
ISSNL: 0004-5411 OR 00045411
bibdate: Thu Apr 29 13:26:36 MDT 2010
bibsource: http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.04.29 13:26:36 MDT
fjournal: Journal of the ACM
acknowledgement: ack-nhfb
articleno: 26
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J401
rawauthor: Alon Altman and Moshe Tennenholtz
rawtitle: An axiomatic approach to personalized ranking systems
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.

keywords:
Advogato; Axiomatic approach; e-Bay reputation system; epinions.com; manipulation; MoleTrust; OpenPGP; PageRank; ranking systems; social networks

rawabstract:
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.

entry:
@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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 12
year: 2010
ISSN: 2150-8097 OR 21508097
bibdate: Fri May 13 14:55:16 MDT 2011
bibsource: http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.05.13 14:55:16 MDT
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
rawauthor: Bahman Bahmani and Abdur Chowdhury and Ashish Goel
rawtitle: Fast incremental and personalized {PageRank}
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.

rawabstract:
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.

entry:
@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",
}


