# bibtab

bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Hsu:2020:BAH
author: Y-Hsin Elsa Hsu and Ya-Ting Yang and Ming-Huang Wang and Yuh-Shan Ho
title: Bibliometric analysis of highly cited publications in health policy and services
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 1
pages: 177--202
year: 2020
DOI: https://doi.org/10.1080/09737766.2020.1816513
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Fri Feb 5 15:52:02 MST 2021
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2021.02.05 15:52:02 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2020.1816513
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Y-Hsin Elsa Hsu and Ya-Ting Yang and Ming-Huang Wang and Yuh-Shan Ho
rawtitle: Bibliometric analysis of highly cited publications in health policy and services
entry:
@Article{Hsu:2020:BAH,
  author =       "Y-Hsin Elsa Hsu and Ya-Ting Yang and Ming-Huang Wang
                 and Yuh-Shan Ho",
  title =        "Bibliometric analysis of highly cited publications in
                 health policy and services",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "14",
  number =       "1",
  pages =        "177--202",
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2020.1816513",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Fri Feb 5 15:52:02 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2020.1816513",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Yue:2020:RIC
author: Chen Yue and Jean-Charles Lamirel and Bernd Markscheffel
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 1
pages: 203--209
year: 2020
DOI: https://doi.org/10.1080/09737766.2020.1838767
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Fri Feb 5 15:52:02 MST 2021
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2021.02.05 15:52:02 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2020.1838767
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Chen Yue and Jean-Charles Lamirel and Bernd Markscheffel
title:
Report of the 15$^th$ International Conference on Webometrics, Informetrics and Scientometrics (WIS) and the 20$^th$ COLLNET Meeting

rawtitle:
Report of the {15$^{th}$ International Conference on Webometrics, Informetrics and Scientometrics (WIS) and the 20$^{th}$ COLLNET Meeting}

entry:
@Article{Yue:2020:RIC,
  author =       "Chen Yue and Jean-Charles Lamirel and Bernd
                 Markscheffel",
  title =        "Report of the {15$^{th}$ International Conference on
                 Webometrics, Informetrics and Scientometrics (WIS) and
                 the 20$^{th}$ COLLNET Meeting}",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "14",
  number =       "1",
  pages =        "203--209",
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2020.1838767",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Fri Feb 5 15:52:02 MST 2021",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2020.1838767",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Kokol:2020:BRA
author: Peter Kokol and Grega Zlahtic and Jernej Zavrsnik and Helena Blazun Vosner
title: Buonocore research in adhesive dentistry: a remarkable sleeping paper
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 211--217
year: 2020
DOI: https://doi.org/10.1080/09737766.2021.1906182
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1906182
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Peter Kokol and Grega Zlahtic and Jernej Zavrsnik and Helena Blazun Vosner
rawtitle: Buonocore research in adhesive dentistry: a remarkable sleeping paper
entry:
@Article{Kokol:2020:BRA,
  author =       "Peter Kokol and Grega Zlahtic and Jernej Zavrsnik and
                 Helena Blazun Vosner",
  title =        "Buonocore research in adhesive dentistry: a remarkable
                 sleeping paper",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "14",
  number =       "2",
  pages =        "211--217",
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1906182",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:04 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1906182",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Wang:2020:BAK
author: Yi-Fen Wang and Ming-Huang Wang and Yuh-Shan Ho
title: A bibliometric analysis of KSHV / HHV8 research
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 219--235
year: 2020
DOI: https://doi.org/10.1080/09737766.2021.1920065
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1920065
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Yi-Fen Wang and Ming-Huang Wang and Yuh-Shan Ho
rawtitle: A bibliometric analysis of {KSHV\slash HHV8} research
entry:
@Article{Wang:2020:BAK,
  author =       "Yi-Fen Wang and Ming-Huang Wang and Yuh-Shan Ho",
  title =        "A bibliometric analysis of {KSHV\slash HHV8}
                 research",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "14",
  number =       "2",
  pages =        "219--235",
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1920065",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:04 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1920065",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Pandey:2020:MGP
author: Sidhartha Sahoo and Shriram Pandey
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 237--256
year: 2020
DOI: https://doi.org/10.1080/09737766.2021.1918031
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1918031
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Sidhartha Sahoo and Shriram Pandey
title:
Measuring growth pattern and publications trends of storm surges research during (1999--2019): a scientometric study

rawtitle:
Measuring growth pattern and publications trends of storm surges research during (1999--2019): a scientometric study

entry:
@Article{Pandey:2020:MGP,
  author =       "Sidhartha Sahoo and Shriram Pandey",
  title =        "Measuring growth pattern and publications trends of
                 storm surges research during (1999--2019): a
                 scientometric study",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "14",
  number =       "2",
  pages =        "237--256",
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1918031",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:04 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1918031",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Zhou:2020:ETM
author: Jin Chen and Wei Zhou
title: The exploration of travel motivation research: a scientometric analysis based on CiteSpace
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 257--283
year: 2020
DOI: https://doi.org/10.1080/09737766.2021.1906779
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1906779
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Jin Chen and Wei Zhou
rawtitle: The exploration of travel motivation research: a scientometric analysis based on {CiteSpace}
entry:
@Article{Zhou:2020:ETM,
  author =       "Jin Chen and Wei Zhou",
  title =        "The exploration of travel motivation research: a
                 scientometric analysis based on {CiteSpace}",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "14",
  number =       "2",
  pages =        "257--283",
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1906779",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:04 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1906779",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Lathabai:2020:MIT
author: Hiran H. Lathabai and Thara Prabhakaran and Susan George and Manoj Changat
title: Mining interdisciplinary trajectories using multiple path analysis
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 285--300
year: 2020
DOI: https://doi.org/10.1080/09737766.2021.1920066
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1920066
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Hiran H. Lathabai and Thara Prabhakaran and Susan George and Manoj Changat
rawtitle: Mining interdisciplinary trajectories using multiple path analysis
entry:
@Article{Lathabai:2020:MIT,
  author =       "Hiran H. Lathabai and Thara Prabhakaran and Susan
                 George and Manoj Changat",
  title =        "Mining interdisciplinary trajectories using multiple
                 path analysis",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "14",
  number =       "2",
  pages =        "285--300",
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  bibdate =      "Mon Feb 7 07:08:04 MST 2022",
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  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1920066",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
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}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Muthappan:2020:BAY
title: Bibliometric analysis of yoga scientific publications: an analysis of PubMed database 1948--2018
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 301--309
year: 2020
DOI: https://doi.org/10.1080/09737766.2021.1913976
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1913976
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawtitle: Bibliometric analysis of yoga scientific publications: an analysis of {PubMed} database 1948--2018
author:
Sendhilkumar Muthappan and Kumaravel Ilangovan and Rathinavel Subramanian and Mantela Durairajan and Rajalakshmi Elumalai

rawauthor:
Sendhilkumar Muthappan and Kumaravel Ilangovan and Rathinavel Subramanian and Mantela Durairajan and Rajalakshmi Elumalai

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  author =       "Sendhilkumar Muthappan and Kumaravel Ilangovan and
                 Rathinavel Subramanian and Mantela Durairajan and
                 Rajalakshmi Elumalai",
  title =        "Bibliometric analysis of yoga scientific publications:
                 an analysis of {PubMed} database 1948--2018",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
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  bibdate =      "Mon Feb 7 07:08:04 MST 2022",
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bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Chhatoi:2020:KMD
author: Biswajit Prasad Chhatoi and Sharada Prasad Sahoo and Durga Prasad Nayak
title: Knowledge mapping in the discipline of financial literacy and charting the future directions
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 311--330
year: 2020
DOI: https://doi.org/10.1080/09737766.2020.1841584
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2020.1841584
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Biswajit Prasad Chhatoi and Sharada Prasad Sahoo and Durga Prasad Nayak
rawtitle: Knowledge mapping in the discipline of financial literacy and charting the future directions
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@Article{Chhatoi:2020:KMD,
  author =       "Biswajit Prasad Chhatoi and Sharada Prasad Sahoo and
                 Durga Prasad Nayak",
  title =        "Knowledge mapping in the discipline of financial
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  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
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bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Rahimmi:2020:DIF
author: Arman Rahimmi
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 331--333
year: 2020
DOI: https://doi.org/10.1080/09737766.2021.1906183
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1906183
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Arman Rahimmi
title:
Drawbacks to $h$-index, as a factor assessing the scientific impact and the scientific credit of a researcher

rawtitle:
Drawbacks to $h$-index, as a factor assessing the scientific impact and the scientific credit of a researcher

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@Article{Rahimmi:2020:DIF,
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  title =        "Drawbacks to $h$-index, as a factor assessing the
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  acknowledgement = ack-nhfb,
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bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Kumbar:2020:WCA
author: B. Niveditha and Mallinath Kumbar
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 14
number: 2
pages: 335--348
year: 2020
DOI: https://doi.org/10.1080/09737766.2021.1915721
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:04 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:04 MST
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  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1938743",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Okagbue:2021:DCC
author: Hilary I. Okagbue and Elvir M. Akhmetshin and Jaime A. Teixeira da Silva
title: Distinct clusters of CiteScore and percentiles in top 1000 journals in Scopus
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 1
pages: 133--143
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1934604
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1934604
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Hilary I. Okagbue and Elvir M. Akhmetshin and Jaime A. Teixeira da Silva
rawtitle: Distinct clusters of {CiteScore} and percentiles in top 1000 journals in {Scopus}
entry:
@Article{Okagbue:2021:DCC,
  author =       "Hilary I. Okagbue and Elvir M. Akhmetshin and Jaime A.
                 Teixeira da Silva",
  title =        "Distinct clusters of {CiteScore} and percentiles in
                 top 1000 journals in {Scopus}",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "1",
  pages =        "133--143",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1934604",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1934604",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Jallow:2021:CAA
author: Amadou W. Jallow and Shin-Da Lee and Yuh-Shan Ho
title: Classic articles in apoptotic research: a bibliometric analysis
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 1
pages: 145--162
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1934605
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1934605
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Amadou W. Jallow and Shin-Da Lee and Yuh-Shan Ho
rawtitle: Classic articles in apoptotic research: a bibliometric analysis
entry:
@Article{Jallow:2021:CAA,
  author =       "Amadou W. Jallow and Shin-Da Lee and Yuh-Shan Ho",
  title =        "Classic articles in apoptotic research: a bibliometric
                 analysis",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "1",
  pages =        "145--162",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1934605",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1934605",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Mota:2021:ECD
author: Luiza Amara Maciel Braga and Fabio Batista Mota
title: Early cancer diagnosis using lab-on-a-chip devices: a bibliometric and network analysis
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 1
pages: 163--196
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1949949
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1949949
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Luiza Amara Maciel Braga and Fabio Batista Mota
rawtitle: Early cancer diagnosis using lab-on-a-chip devices: a bibliometric and network analysis
entry:
@Article{Mota:2021:ECD,
  author =       "Luiza Amara Maciel Braga and Fabio Batista Mota",
  title =        "Early cancer diagnosis using lab-on-a-chip devices: a
                 bibliometric and network analysis",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "1",
  pages =        "163--196",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1949949",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1949949",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Borgohain:2021:ABL
author: Dhruba Jyoti Borgohain and Manoj Kumar Verma and Mohammad Nazim and Mayukh Sarkar
title: Application of Bradford's law of scattering and Leimkuhler model to information science literature
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 1
pages: 197--212
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1943041
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1943041
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Dhruba Jyoti Borgohain and Manoj Kumar Verma and Mohammad Nazim and Mayukh Sarkar
rawtitle:
Application of {Bradford}'s law of scattering and {Leimkuhler} model to information science literature

entry:
@Article{Borgohain:2021:ABL,
  author =       "Dhruba Jyoti Borgohain and Manoj Kumar Verma and
                 Mohammad Nazim and Mayukh Sarkar",
  title =        "Application of {Bradford}'s law of scattering and
                 {Leimkuhler} model to information science literature",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "1",
  pages =        "197--212",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1943041",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1943041",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Rousseau:2021:NNM
author: Ronald Rousseau
title: booktitle Naukometriya, Nalimov and Mul'chenko
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 1
pages: 213--224
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1943042
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1943042
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Ronald Rousseau
rawtitle: {{\booktitle{Naukometriya}}}, {Nalimov} and {Mul'chenko}
abstract:
In this article, we try to find out what Vasiliy Nalimov and Zinaida Mul'chenko exactly wrote in their famous book Naukometriya, the Study of the Development of Science as an Information Process (1969). Afterward, the term Naukometriya became known in the West as Scientometrics.par It is often mentioned that Nalimov invented the term scientometrics, usually with reference to his book published in 1969. Shouldn't Mul'chenko be mentioned as one of the inventors of this term? We show that referring to Naukometriya is a case of wrong reference, as Nalimov introduced the term already in 1966.par We further place the term scientometrics in the context of the science of science and of Soviet science during that period.

rawabstract:
In this article, we try to find out what Vasiliy Nalimov and Zinaida Mul'chenko exactly wrote in their famous book Naukometriya, the Study of the Development of Science as an Information Process (1969). Afterward, the term Naukometriya became known in the West as Scientometrics.\par It is often mentioned that Nalimov invented the term scientometrics, usually with reference to his book published in 1969. Shouldn't Mul'chenko be mentioned as one of the inventors of this term? We show that referring to Naukometriya is a case of wrong reference, as Nalimov introduced the term already in 1966.\par We further place the term scientometrics in the context of the science of science and of Soviet science during that period.

entry:
@Article{Rousseau:2021:NNM,
  author =       "Ronald Rousseau",
  title =        "{{\booktitle{Naukometriya}}}, {Nalimov} and
                 {Mul'chenko}",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "1",
  pages =        "213--224",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1943042",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1943042",
  abstract =     "In this article, we try to find out what Vasiliy
                 Nalimov and Zinaida Mul'chenko exactly wrote in their
                 famous book Naukometriya, the Study of the Development
                 of Science as an Information Process (1969). Afterward,
                 the term Naukometriya became known in the West as
                 Scientometrics.\par

                 It is often mentioned that Nalimov invented the term
                 scientometrics, usually with reference to his book
                 published in 1969. Shouldn't Mul'chenko be mentioned as
                 one of the inventors of this term? We show that
                 referring to Naukometriya is a case of wrong reference,
                 as Nalimov introduced the term already in 1966.\par

                 We further place the term scientometrics in the context
                 of the science of science and of Soviet science during
                 that period.",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Mosallaie:2021:DEA
author: Shahab Mosallaie and Mahdi Rad and Andrea Schiffauerova and Ashkan Ebadi
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 2
pages: 225--240
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1958659
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1958659
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Shahab Mosallaie and Mahdi Rad and Andrea Schiffauerova and Ashkan Ebadi
title:
Discovering the evolution of artificial intelligence in cancer research using dynamic topic modeling

rawtitle:
Discovering the evolution of artificial intelligence in cancer research using dynamic topic modeling

entry:
@Article{Mosallaie:2021:DEA,
  author =       "Shahab Mosallaie and Mahdi Rad and Andrea
                 Schiffauerova and Ashkan Ebadi",
  title =        "Discovering the evolution of artificial intelligence
                 in cancer research using dynamic topic modeling",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "2",
  pages =        "225--240",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1958659",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1958659",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Stoupas:2021:WUR
author: Georgios Stoupas and Antonis Sidiropoulos and Dimitrios Katsaros and Yannis Manolopoulos
title: When universities rise (rank) high into the skyline
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 2
pages: 241--258
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1955419
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1955419
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Georgios Stoupas and Antonis Sidiropoulos and Dimitrios Katsaros and Yannis Manolopoulos
rawtitle: When universities rise (rank) high into the skyline
entry:
@Article{Stoupas:2021:WUR,
  author =       "Georgios Stoupas and Antonis Sidiropoulos and
                 Dimitrios Katsaros and Yannis Manolopoulos",
  title =        "When universities rise (rank) high into the skyline",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "2",
  pages =        "241--258",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1955419",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1955419",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Pou:2021:AMT
author: K. R. Jolvis Pou and Vijaya Raghavan and Muthukumaran Packirisamy
title: Applications of microfluidic technology in food sector: a bibliometric analysis
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 2
pages: 259--285
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1989989
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1989989
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: K. R. Jolvis Pou and Vijaya Raghavan and Muthukumaran Packirisamy
rawtitle: Applications of microfluidic technology in food sector: a bibliometric analysis
entry:
@Article{Pou:2021:AMT,
  author =       "K. R. Jolvis Pou and Vijaya Raghavan and Muthukumaran
                 Packirisamy",
  title =        "Applications of microfluidic technology in food
                 sector: a bibliometric analysis",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "2",
  pages =        "259--285",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1989989",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1989989",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Wahid:2021:GLS
author: Nazia Wahid and Nosheen Fatima Warraich and Muzammil Tahira
title: Group level scientometric analysis of Pakistani authors
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 2
pages: 287--304
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1960219
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1960219
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Nazia Wahid and Nosheen Fatima Warraich and Muzammil Tahira
rawtitle: Group level scientometric analysis of {Pakistani} authors
entry:
@Article{Wahid:2021:GLS,
  author =       "Nazia Wahid and Nosheen Fatima Warraich and Muzammil
                 Tahira",
  title =        "Group level scientometric analysis of {Pakistani}
                 authors",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "15",
  number =       "2",
  pages =        "287--304",
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1080/09737766.2021.1960219",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Mon Feb 7 07:08:05 MST 2022",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1960219",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Fanourgiakis:2021:ERB
author: J. Fanourgiakis and C. Panagiotakis and I. Konstantourakis
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 15
number: 2
pages: 305--322
year: 2021
DOI: https://doi.org/10.1080/09737766.2021.1978826
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Mon Feb 7 07:08:05 MST 2022
bibsource: http://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2022.02.07 07:08:05 MST
URL: http://www.tandfonline.com/doi/abs/10.1080/09737766.2021.1978826
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: J. Fanourgiakis and C. Panagiotakis and I. Konstantourakis
title:
Exploring the research-based ranking and the university impact in Greece: a case of peripheral departments of management and economics

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rawtitle: Predicted correlation
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author: Anonymous
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
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number: 1
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rawauthor: Anonymous
title:
Scientometric mapping of organic farming research performance in the global perspective during 2010--2019

rawtitle:
Scientometric mapping of organic farming research performance in the global perspective during 2010--2019

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bibtype: article
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label: Anonymous:2022:MIC
author: Anonymous
title: Measuring the impact of co-author count on citation count of research publications
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number: 1
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bibdate: Thu Oct 10 08:10:20 MDT 2024
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rawtitle: Measuring the impact of co-author count on citation count of research publications
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bibtype: article
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label: Anonymous:2022:DIG
author: Anonymous
title: The development of Indonesian e-Government: a bibliometric analysis
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volume: 16
number: 1
pages: 49--74
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
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bibdate: Thu Oct 10 08:10:20 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:20 MDT
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rawtitle: The development of {Indonesian} e-Government: a bibliometric analysis
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bibtype: article
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label: Anonymous:2022:UTA
author: Anonymous
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 1
pages: 75--116
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
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bibdate: Thu Oct 10 08:10:20 MDT 2024
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bibtimestamp: 2024.10.10 08:10:20 MDT
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acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
title:
Unified theory of acceptance and use of technology (UTAUT) in mobile learning adoption : Systematic literature review and bibliometric analysis

rawtitle:
Unified theory of acceptance and use of technology {(UTAUT)} in mobile learning adoption : Systematic literature review and bibliometric analysis

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  title =        "Unified theory of acceptance and use of technology
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  number =       "1",
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bibtype: article
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label: Anonymous:2022:PAM
author: Anonymous
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 1
pages: 117--124
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
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bibdate: Thu Oct 10 08:10:20 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:20 MDT
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journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
title:
Predicting access mode of multidisciplinary and library and information sciences journals using machine learning

rawtitle:
Predicting access mode of multidisciplinary and library and information sciences journals using machine learning

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bibtype: article
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label: Anonymous:2022:SAV
author: Anonymous
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
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pages: 125--143
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ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:20 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:20 MDT
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acknowledgement: ack-nhfb
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rawauthor: Anonymous
title:
Scientometric analysis and visualisation of global information literacy from higher education perspective

rawtitle:
Scientometric analysis and visualisation of global information literacy from higher education perspective

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  author =       "Anonymous",
  title =        "Scientometric analysis and visualisation of global
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  volume =       "16",
  number =       "1",
  pages =        "125--143",
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  ISSN-L =       "0973-7766",
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bibtype: article
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label: Anonymous:2022:SMG
author: Anonymous
title: Scientometric mapping of global publication trends in health informatics domain
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volume: 16
number: 1
pages: 145--159
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:20 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:20 MDT
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acknowledgement: ack-nhfb
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rawauthor: Anonymous
rawtitle: Scientometric mapping of global publication trends in health informatics domain
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bibtype: article
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label: Anonymous:2022:BCT
author: Anonymous
title: Bibliographic coupling and types of centralities: a review of ASME journals --- 2000 to 2020
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 1
pages: 161--186
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:20 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:20 MDT
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rawtitle: Bibliographic coupling and types of centralities: a review of {ASME} journals --- 2000 to 2020
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bibtype: article
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label: Anonymous:2022:NMP
author: Anonymous
title: Novel model for predicting the future volume of research articles on applications of Quantum Dots
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volume: 16
number: 1
pages: 187--213
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
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bibdate: Thu Oct 10 08:10:20 MDT 2024
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bibtimestamp: 2024.10.10 08:10:20 MDT
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rawtitle: Novel model for predicting the future volume of research articles on applications of Quantum Dots
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bibtype: article
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label: Anonymous:2022:RDV
author: Anonymous
title: Research discovery and visualization using ResearchRabbit: a use case of AI in libraries
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 215--237
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
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bibtype: article
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author: Anonymous
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volume: 16
number: 2
pages: 239--251
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
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bibtimestamp: 2024.10.10 08:10:21 MDT
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acknowledgement: ack-nhfb
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rawauthor: Anonymous
title:
Are indexing, metrics and the article processing charges (APC) of Hindawi open access journals linked?

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Are indexing, metrics and the article processing charges {(APC)} of {Hindawi} open access journals linked?

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bibtype: article
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label: Anonymous:2022:HGR
author: Anonymous
title: Hegemony in global rankings: a Gramscian analysis of bibliometric indices and ranking results
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volume: 16
number: 2
pages: 253--277
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
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bibtype: article
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label: Anonymous:2022:BMS
author: Anonymous
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 279--304
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
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bibdate: Thu Oct 10 08:10:21 MDT 2024
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fjournal: Collnet Journal of Scientometrics and Information Management
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title:
Bibliometric measures in social sciences and humanities from different sources based on Brazilian data

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Bibliometric measures in social sciences and humanities from different sources based on {Brazilian} data

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  fjournal =     "Collnet Journal of Scientometrics and Information
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  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Anonymous:2022:TIM
author: Anonymous
title: $h$-type indices for multiple authorship papers based on ``relative first author'' principle
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 305--330
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:21 MDT
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
rawtitle: $h$-type indices for multiple authorship papers based on ``relative first author'' principle
entry:
@Article{Anonymous:2022:TIM,
  author =       "Anonymous",
  title =        "$h$-type indices for multiple authorship papers based
                 on ``relative first author'' principle",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "16",
  number =       "2",
  pages =        "305--330",
  year =         "2022",
  CODEN =        "????",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Thu Oct 10 08:10:21 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Anonymous:2022:CRP
author: Anonymous
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 331--352
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:21 MDT
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
title:
A critical review of the proposed academic performance indicators for the assessment of individual researchers in Hungary

rawtitle:
A critical review of the proposed academic performance indicators for the assessment of individual researchers in {Hungary}

entry:
@Article{Anonymous:2022:CRP,
  author =       "Anonymous",
  title =        "A critical review of the proposed academic performance
                 indicators for the assessment of individual researchers
                 in {Hungary}",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "16",
  number =       "2",
  pages =        "331--352",
  year =         "2022",
  CODEN =        "????",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Thu Oct 10 08:10:21 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Anonymous:2022:SRG
author: Anonymous
title: Scientometric review of global squaraine dye research
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 353--370
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:21 MDT
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
rawtitle: Scientometric review of global squaraine dye research
entry:
@Article{Anonymous:2022:SRG,
  author =       "Anonymous",
  title =        "Scientometric review of global squaraine dye
                 research",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "16",
  number =       "2",
  pages =        "353--370",
  year =         "2022",
  CODEN =        "????",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Thu Oct 10 08:10:21 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Anonymous:2022:RBN
author: Anonymous
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 371--388
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:21 MDT
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
title:
Relationship between number of downloads and three journal-based metrics of 11 subject categories among 1575 Springer booktitle Nature journals

rawtitle:
Relationship between number of downloads and three journal-based metrics of 11 subject categories among 1575 {Springer} {{\booktitle{Nature}}} journals

entry:
@Article{Anonymous:2022:RBN,
  author =       "Anonymous",
  title =        "Relationship between number of downloads and three
                 journal-based metrics of 11 subject categories among
                 1575 {Springer} {{\booktitle{Nature}}} journals",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "16",
  number =       "2",
  pages =        "371--388",
  year =         "2022",
  CODEN =        "????",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Thu Oct 10 08:10:21 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Anonymous:2022:PBB
author: Anonymous
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 389--405
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:21 MDT
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
title:
A PubMed based bibliometric study on Covid-19 and SARS Corona Virus vaccine and alternative medicine-based research and development

rawtitle:
A {PubMed} based bibliometric study on {Covid-19} and {SARS Corona Virus} vaccine and alternative medicine-based research and development

entry:
@Article{Anonymous:2022:PBB,
  author =       "Anonymous",
  title =        "A {PubMed} based bibliometric study on {Covid-19} and
                 {SARS Corona Virus} vaccine and alternative
                 medicine-based research and development",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "16",
  number =       "2",
  pages =        "389--405",
  year =         "2022",
  CODEN =        "????",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Thu Oct 10 08:10:21 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Anonymous:2022:GIN
author: Anonymous
title: em GH-index: a new index for the assessment of scientists
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 407--464
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:21 MDT
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
rawtitle: {{\em GH\/}}-index: a new index for the assessment of scientists
entry:
@Article{Anonymous:2022:GIN,
  author =       "Anonymous",
  title =        "{{\em GH\/}}-index: a new index for the assessment of
                 scientists",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "16",
  number =       "2",
  pages =        "407--464",
  year =         "2022",
  CODEN =        "????",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Thu Oct 10 08:10:21 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Anonymous:2022:GDD
author: Anonymous
title: Gender differences, data carpentry and bibliometric studies in Mathematics
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 465--476
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:21 MDT
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
rawtitle: Gender differences, data carpentry and bibliometric studies in Mathematics
entry:
@Article{Anonymous:2022:GDD,
  author =       "Anonymous",
  title =        "Gender differences, data carpentry and bibliometric
                 studies in Mathematics",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "16",
  number =       "2",
  pages =        "465--476",
  year =         "2022",
  CODEN =        "????",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Thu Oct 10 08:10:21 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: article
filename: collnet-j-scientometrics-inf-manage.bib
label: Anonymous:2022:ECS
author: Anonymous
title: Examination of e-commerce studies between 2016 and 2021 with science mapping and network analysis
journal: j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE
volume: 16
number: 2
pages: 477--507
year: 2022
ISSN: 0973-7766 (print), 2168-930X (electronic) OR 09737766 (print), 2168930X (electronic)
ISSNL: 0973-7766 OR 09737766
bibdate: Thu Oct 10 08:10:21 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib
bibtimestamp: 2024.10.10 08:10:21 MDT
fjournal: Collnet Journal of Scientometrics and Information Management
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/tsim20
rawauthor: Anonymous
rawtitle: Examination of e-commerce studies between 2016 and 2021 with science mapping and network analysis
entry:
@Article{Anonymous:2022:ECS,
  author =       "Anonymous",
  title =        "Examination of e-commerce studies between 2016 and
                 2021 with science mapping and network analysis",
  journal =      j-COLLNET-J-SCIENTOMETRICS-INF-MANAGE,
  volume =       "16",
  number =       "2",
  pages =        "477--507",
  year =         "2022",
  CODEN =        "????",
  ISSN =         "0973-7766 (print), 2168-930X (electronic)",
  ISSN-L =       "0973-7766",
  bibdate =      "Thu Oct 10 08:10:21 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/collnet-j-scientometrics-inf-manage.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Collnet Journal of Scientometrics and Information
                 Management",
  journal-URL =  "http://www.tandfonline.com/loi/tsim20",
}


bibtype: book
filename: pagerank.bib
label: Leontief:1941:SAE
author: Wassily W. Leontief
title: The Structure of American Economy, 1919--1929: an empirical application of equilibrium analysis
publisher: pub-HARVARD
address: pub-HARVARD:adr
pages: xi + 181
year: 1941
bibdate: Fri Feb 19 15:19:39 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:19:39 ???
URL: http://nobelprize.org/nobel_prizes/economics/laureates/1973/
acknowledgement: ack-nhfb
rawauthor: Wassily W. Leontief
rawtitle: The Structure of {American} Economy, 1919--1929: an empirical application of equilibrium analysis
remark:
The author was awarded the Nobel Prize in Economics in 1973. Franceschet cite Franceschet:2010:PSS traces the PageRank algorithm back to this book.

entry:
@Book{Leontief:1941:SAE,
  author =       "Wassily W. Leontief",
  title =        "The Structure of {American} Economy, 1919--1929: an
                 empirical application of equilibrium analysis",
  publisher =    pub-HARVARD,
  address =      pub-HARVARD:adr,
  pages =        "xi + 181",
  year =         "1941",
  LCCN =         "????",
  bibdate =      "Fri Feb 19 15:19:39 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://nobelprize.org/nobel_prizes/economics/laureates/1973/",
  acknowledgement = ack-nhfb,
  remark =       "The author was awarded the Nobel Prize in Economics in
                 1973. Franceschet \cite{Franceschet:2010:PSS} traces
                 the PageRank algorithm back to this book.",
}


bibtype: article
filename: pagerank.bib
label: Marchiori:1997:QCI
author: Massimo Marchiori
title: The quest for correct information on the Web: Hyper search engines
journal: j-COMP-NET-ISDN
volume: 29
number: 8--13
pages: 1225--1236
day: 30
month: sep
monthnumber: 9
year: 1997
CODEN: CNISE9
ISSN: 0169-7552 (print), 1879-2324 (electronic) OR 01697552 (print), 18792324 (electronic)
ISSNL: 0169-7552 OR 01697552
bibdate: Fri Sep 24 20:21:54 MDT 1999
bibtimestamp: 1999.09.24 20:21:54 MDT
fjournal: Computer Networks and ISDN Systems
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/01697552
rawauthor: Massimo Marchiori
rawtitle: The quest for correct information on the {Web}: Hyper search engines
bibsource:
http://www.elsevier.com/cgi-bin/cas/tree/store/cna/cas_free/browse/browse.cgi?year=1997&volume=29&issue=08-13; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
http://www.computerworld.com/s/article/9222018/Italian_mathematician_prepares_to_challenge_Google; http://www.elsevier.com/cgi-bin/cas/tree/store/comnet/cas_sub/browse/browse.cgi?year=1997&volume=29&issue=08-13&aid=1711

remark:
This article is claimed in a 2011-11-21 ComputerWorld story to be a precursor of the Google PageRank algorithm, although it refers to it as a 1996 conference article.

entry:
@Article{Marchiori:1997:QCI,
  author =       "Massimo Marchiori",
  title =        "The quest for correct information on the {Web}: Hyper
                 search engines",
  journal =      j-COMP-NET-ISDN,
  volume =       "29",
  number =       "8--13",
  pages =        "1225--1236",
  day =          "30",
  month =        sep,
  year =         "1997",
  CODEN =        "CNISE9",
  ISSN =         "0169-7552 (print), 1879-2324 (electronic)",
  ISSN-L =       "0169-7552",
  bibdate =      "Fri Sep 24 20:21:54 MDT 1999",
  bibsource =    "http://www.elsevier.com/cgi-bin/cas/tree/store/cna/cas_free/browse/browse.cgi?year=1997&volume=29&issue=08-13;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.computerworld.com/s/article/9222018/Italian_mathematician_prepares_to_challenge_Google;
                 http://www.elsevier.com/cgi-bin/cas/tree/store/comnet/cas_sub/browse/browse.cgi?year=1997&volume=29&issue=08-13&aid=1711",
  acknowledgement = ack-nhfb,
  fjournal =     "Computer Networks and ISDN Systems",
  journal-URL =  "http://www.sciencedirect.com/science/journal/01697552",
  remark =       "This article is claimed in a 2011-11-21 ComputerWorld
                 story to be a precursor of the Google PageRank
                 algorithm, although it refers to it as a 1996
                 conference article.",
}


bibtype: article
filename: pagerank.bib
label: Brin:1998:ALS
author: Sergey Brin and Lawrence Page
title: The anatomy of a large-scale hypertextual Web search engine
journal: j-COMP-NET-ISDN
volume: 30
number: 1--7
pages: 107--117
day: 1
month: apr
monthnumber: 4
year: 1998
CODEN: CNISE9
ISSN: 0169-7552 (print), 1879-2324 (electronic) OR 01697552 (print), 18792324 (electronic)
ISSNL: 0169-7552 OR 01697552
bibdate: Fri Sep 24 20:22:05 MDT 1999
bibtimestamp: 1999.09.24 20:22:05 MDT
URL: http://www.elsevier.com/cas/tree/store/comnet/sub/1998/30/1-7/1921.pdf
fjournal: Computer Networks and ISDN Systems
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/01697552
rawauthor: Sergey Brin and Lawrence Page
rawtitle: The anatomy of a large-scale hypertextual {Web} search engine
bibsource:
http://www.elsevier.com/cgi-bin/cas/tree/store/cna/cas_free/browse/browse.cgi?year=1998&volume=30&issue=1-7; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Brin:1998:ALS,
  author =       "Sergey Brin and Lawrence Page",
  title =        "The anatomy of a large-scale hypertextual {Web} search
                 engine",
  journal =      j-COMP-NET-ISDN,
  volume =       "30",
  number =       "1--7",
  pages =        "107--117",
  day =          "1",
  month =        apr,
  year =         "1998",
  CODEN =        "CNISE9",
  ISSN =         "0169-7552 (print), 1879-2324 (electronic)",
  ISSN-L =       "0169-7552",
  bibdate =      "Fri Sep 24 20:22:05 MDT 1999",
  bibsource =    "http://www.elsevier.com/cgi-bin/cas/tree/store/cna/cas_free/browse/browse.cgi?year=1998&volume=30&issue=1-7;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.elsevier.com/cas/tree/store/comnet/sub/1998/30/1-7/1921.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "Computer Networks and ISDN Systems",
  journal-URL =  "http://www.sciencedirect.com/science/journal/01697552",
}


bibtype: techreport
filename: pagerank.bib
label: Page:1998:PCR
author: Lawrence Page and Sergey Brin and Rajeev Motwani and Terry Winograd
title: The PageRank Citation Ranking: Bringing Order to the Web
institution: Stanford Digital Library Technologies Project, Stanford University
address: Stanford, CA, USA
pages: 17
day: 11
month: nov
monthnumber: 11
year: 1998
bibdate: Thu Oct 24 15:13:54 2002
bibtimestamp: 2002.10.24 15:13:54 ???
URL: http://dbpubs.stanford.edu/pub/1999-66; http://ilpubs.Stanford.edu:8090/422/
acknowledgement: ack-nhfb
annote: This is the Google search algorithm.
rawauthor: Lawrence Page and Sergey Brin and Rajeev Motwani and Terry Winograd
rawtitle: The {PageRank} Citation Ranking: Bringing Order to the Web
bibsource:
https://www.math.utah.edu/pub/tex/bib/master.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

abstract:
The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

rawabstract:
The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

entry:
@TechReport{Page:1998:PCR,
  author =       "Lawrence Page and Sergey Brin and Rajeev Motwani and
                 Terry Winograd",
  title =        "The {PageRank} Citation Ranking: Bringing Order to the
                 Web",
  institution =  "Stanford Digital Library Technologies Project,
                 Stanford University",
  address =      "Stanford, CA, USA",
  pages =        "17",
  day =          "11",
  month =        nov,
  year =         "1998",
  bibdate =      "Thu Oct 24 15:13:54 2002",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/master.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://dbpubs.stanford.edu/pub/1999-66;
                 http://ilpubs.Stanford.edu:8090/422/",
  abstract =     "The importance of a Web page is an inherently
                 subjective matter, which depends on the readers
                 interests, knowledge and attitudes. But there is still
                 much that can be said objectively about the relative
                 importance of Web pages. This paper describes PageRank,
                 a mathod for rating Web pages objectively and
                 mechanically, effectively measuring the human interest
                 and attention devoted to them. We compare PageRank to
                 an idealized random Web surfer. We show how to
                 efficiently compute PageRank for large numbers of
                 pages. And, we show how to apply PageRank to search and
                 to user navigation.",
  acknowledgement = ack-nhfb,
  annote =       "This is the Google search algorithm.",
}


bibtype: techreport
filename: pagerank.bib
label: Page:1999:PCR
author: Lawrence Page and Sergey Brin and Rajeev Motwani and Terry Winograd
title: The PageRank Citation Ranking: Bringing Order to the Web
type: Technical Report
number: 1999-66
institution: Stanford Digital Library Technologies Project, Stanford University
address: Stanford, CA, USA
pages: 17
year: 1999
bibdate: Tue Aug 11 17:32:19 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:32:19 ???
acknowledgement: ack-nhfb
rawauthor: Lawrence Page and Sergey Brin and Rajeev Motwani and Terry Winograd
rawtitle: The {PageRank} Citation Ranking: Bringing Order to the Web
entry:
@TechReport{Page:1999:PCR,
  author =       "Lawrence Page and Sergey Brin and Rajeev Motwani and
                 Terry Winograd",
  title =        "The {PageRank} Citation Ranking: Bringing Order to the
                 Web",
  type =         "Technical Report",
  number =       "1999-66",
  institution =  "Stanford Digital Library Technologies Project,
                 Stanford University",
  address =      "Stanford, CA, USA",
  year =         "1999",
  bibdate =      "Tue Aug 11 17:32:19 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Finkelstein:2001:PSC
title: Placing search in context: the concept revisited
crossref: ACM:2001:CPT
pages: 406--414
year: 2001
DOI: https://doi.org/10.1145/371920.372094
bibdate: Mon May 10 14:07:25 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.10 14:07:25 ???
acknowledgement: ack-nhfb
rawtitle: Placing search in context: the concept revisited
author:
Lev Finkelstein and Evgeniy Gabrilovich and Yossi Matias and Ehud Rivlin and Zach Solan and Gadi Wolfman and Eytan Ruppin

rawauthor:
Lev Finkelstein and Evgeniy Gabrilovich and Yossi Matias and Ehud Rivlin and Zach Solan and Gadi Wolfman and Eytan Ruppin

entry:
@InProceedings{Finkelstein:2001:PSC,
  author =       "Lev Finkelstein and Evgeniy Gabrilovich and Yossi
                 Matias and Ehud Rivlin and Zach Solan and Gadi Wolfman
                 and Eytan Ruppin",
  title =        "Placing search in context: the concept revisited",
  crossref =     "ACM:2001:CPT",
  pages =        "406--414",
  year =         "2001",
  DOI =          "https://doi.org/10.1145/371920.372094",
  bibdate =      "Mon May 10 14:07:25 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Kruschwitz:2001:WKD
author: U. Kruschwitz
title: World knowledge for the domain of your choice
crossref: Bahill:2001:IIC
pages: 555--560
year: 2001
DOI: https://doi.org/10.1109/ICSMC.2001.969872
bibdate: Thu May 06 13:31:24 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 13:31:24 ???
acknowledgement: ack-nhfb
rawauthor: U. Kruschwitz
rawtitle: World knowledge for the domain of your choice
booktitle:
IEEE International Conference on Systems, Man, and Cybernetics, 2001, 7--10 October, 2001, Tucson, AZ

abstract:
Modern Web search engines access large parts of the publicly indexable Web. Relevant sites can be found easily thanks to advanced techniques such as Google's PageRank algorithm. However, a common problem remains the large number of matching documents being returned even for fairly specific queries. The same problem can be observed in domains that are more limited like intranets or local Web sites. By enriching a search engine with knowledge about the domain one could provide much more feedback for a query than just a list of matches, such as a number of useful discriminating terms, that would allow the user to constrain the query. We present a way of building such a domain model automatically by analyzing the markup of the source data. We will illustrate this with some examples taken from our sample domain.

rawabstract:
Modern Web search engines access large parts of the publicly indexable Web. Relevant sites can be found easily thanks to advanced techniques such as Google's PageRank algorithm. However, a common problem remains the large number of matching documents being returned even for fairly specific queries. The same problem can be observed in domains that are more limited like intranets or local Web sites. By enriching a search engine with knowledge about the domain one could provide much more feedback for a query than just a list of matches, such as a number of useful discriminating terms, that would allow the user to constrain the query. We present a way of building such a domain model automatically by analyzing the markup of the source data. We will illustrate this with some examples taken from our sample domain.

rawbooktitle:
{IEEE International Conference on Systems, Man, and Cybernetics, 2001, 7--10 October, 2001, Tucson, AZ}

entry:
@InProceedings{Kruschwitz:2001:WKD,
  author =       "U. Kruschwitz",
  booktitle =    "{IEEE International Conference on Systems, Man, and
                 Cybernetics, 2001, 7--10 October, 2001, Tucson, AZ}",
  title =        "World knowledge for the domain of your choice",
  crossref =     "Bahill:2001:IIC",
  pages =        "555--560",
  year =         "2001",
  DOI =          "https://doi.org/10.1109/ICSMC.2001.969872",
  bibdate =      "Thu May 06 13:31:24 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Modern Web search engines access large parts of the
                 publicly indexable Web. Relevant sites can be found
                 easily thanks to advanced techniques such as Google's
                 PageRank algorithm. However, a common problem remains
                 the large number of matching documents being returned
                 even for fairly specific queries. The same problem can
                 be observed in domains that are more limited like
                 intranets or local Web sites. By enriching a search
                 engine with knowledge about the domain one could
                 provide much more feedback for a query than just a list
                 of matches, such as a number of useful discriminating
                 terms, that would allow the user to constrain the
                 query. We present a way of building such a domain model
                 automatically by analyzing the markup of the source
                 data. We will illustrate this with some examples taken
                 from our sample domain.",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Miller:2001:MKH
editor: Donald H. Kraft
title: Modifications of Kleinberg's HITS algorithm using matrix exponentiation and web log records
publisher: pub-ACM
address: pub-ACM:adr
pages: 444--445
year: 2001
DOI: https://doi.org/10.1145/383952.384086
ISBN: 1-58113-331-6 OR 1581133316
ISBN13: 978-1-58113-331-8 OR 9781581133318
LCCN: QA76.9.D3 I552 2001; Z699.A1
bibdate: Tue Aug 11 17:26:34 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:26:34 ???
acknowledgement: ack-nhfb
raweditor: Donald H. Kraft
rawtitle: Modifications of {Kleinberg}'s {HITS} algorithm using matrix exponentiation and web log records
author:
Joel C. Miller and Gregory Rae and Fred Schaefer and Lesley A. Ward and Thomas LoFaro and Ayman Farahat

booktitle:
Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 01: New Orleans, Louisiana, USA, September 9--13, 2001

rawauthor:
Joel C. Miller and Gregory Rae and Fred Schaefer and Lesley A. Ward and Thomas LoFaro and Ayman Farahat

rawbooktitle:
{Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 01: New Orleans, Louisiana, USA, September 9--13, 2001}

entry:
@InProceedings{Miller:2001:MKH,
  author =       "Joel C. Miller and Gregory Rae and Fred Schaefer and
                 Lesley A. Ward and Thomas LoFaro and Ayman Farahat",
  editor =       "Donald H. Kraft",
  booktitle =    "{Proceedings of the 24th Annual International ACM
                 SIGIR Conference on Research and Development in
                 Information Retrieval, SIGIR 01: New Orleans,
                 Louisiana, USA, September 9--13, 2001}",
  title =        "Modifications of {Kleinberg}'s {HITS} algorithm using
                 matrix exponentiation and web log records",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "444--445",
  year =         "2001",
  DOI =          "https://doi.org/10.1145/383952.384086",
  ISBN =         "1-58113-331-6",
  ISBN-13 =      "978-1-58113-331-8",
  LCCN =         "QA76.9.D3 I552 2001; Z699.A1",
  bibdate =      "Tue Aug 11 17:26:34 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Ng:2001:SAL
author: Andrew Y. Ng and Alice X. Zheng and Michael I. Jordan
title: Stable algorithms for link analysis
crossref: Croft:2001:PAI
pages: 258--266
year: 2001
DOI: https://doi.org/10.1145/383952.384003
bibdate: Wed Jun 01 18:24:42 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.01 18:24:42 ???
acknowledgement: ack-nhfb
rawauthor: Andrew Y. Ng and Alice X. Zheng and Michael I. Jordan
rawtitle: Stable algorithms for link analysis
abstract:
The Kleinberg HITS and the Google PageRank algorithms are eigenvector methods for identifying ``authoritative'' or ``influential'' articles, given hyperlink or citation information. That such algorithms should give reliable or consistent answers is surely a desideratum, and in cite ijcaiPaper, we analyzed when they can be expected to give stable rankings under small perturbations to the linkage patterns. In this paper, we extend the analysis and show how it gives insight into ways of designing stable link analysis methods. This in turn motivates two new algorithms, whose performance we study empirically using citation data and web hyperlink data.

rawabstract:
The Kleinberg HITS and the Google PageRank algorithms are eigenvector methods for identifying ``authoritative'' or ``influential'' articles, given hyperlink or citation information. That such algorithms should give reliable or consistent answers is surely a desideratum, and in \cite{ijcaiPaper}, we analyzed when they can be expected to give stable rankings under small perturbations to the linkage patterns. In this paper, we extend the analysis and show how it gives insight into ways of designing stable link analysis methods. This in turn motivates two new algorithms, whose performance we study empirically using citation data and web hyperlink data.

entry:
@InProceedings{Ng:2001:SAL,
  author =       "Andrew Y. Ng and Alice X. Zheng and Michael I.
                 Jordan",
  title =        "Stable algorithms for link analysis",
  crossref =     "Croft:2001:PAI",
  pages =        "258--266",
  year =         "2001",
  DOI =          "https://doi.org/10.1145/383952.384003",
  bibdate =      "Wed Jun 01 18:24:42 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The Kleinberg HITS and the Google PageRank algorithms
                 are eigenvector methods for identifying
                 ``authoritative'' or ``influential'' articles, given
                 hyperlink or citation information. That such algorithms
                 should give reliable or consistent answers is surely a
                 desideratum, and in \cite{ijcaiPaper}, we analyzed when
                 they can be expected to give stable rankings under
                 small perturbations to the linkage patterns. In this
                 paper, we extend the analysis and show how it gives
                 insight into ways of designing stable link analysis
                 methods. This in turn motivates two new algorithms,
                 whose performance we study empirically using citation
                 data and web hyperlink data.",
  acknowledgement = ack-nhfb,
}


bibtype: misc
filename: pagerank.bib
label: Page:2001:MNR
author: Lawrence Page
title: Method for node ranking in a linked database
pages: 258--266
day: 4
month: sep
monthnumber: 9
year: 2001
bibdate: Thu Jun 02 08:24:11 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.02 08:24:11 ???
note: Filed January 9, 1998. Expires around January 9, 2018.
URL: http://patft.uspto.gov/netahtml/PTO/srchnum.htm
remark: This may be the main patent behind the Google search engine.
acknowledgement: ack-nhfb
howpublished: US Patent 6,285,999
rawauthor: Lawrence Page
rawnote: Filed January 9, 1998. Expires around January 9, 2018.
rawtitle: Method for node ranking in a linked database
abstract:
A method assigns importance ranks to nodes in a linked database, such as any database of documents containing citations, the world wide web or any other hypermedia database. The rank assigned to a document is calculated from the ranks of documents citing it. In addition, the rank of a document is calculated from a constant representing the probability that a browser through the database will randomly jump to the document. The method is particularly useful in enhancing the performance of search engine results for hypermedia databases, such as the world wide web, whose documents have a large variation in quality.

rawabstract:
A method assigns importance ranks to nodes in a linked database, such as any database of documents containing citations, the world wide web or any other hypermedia database. The rank assigned to a document is calculated from the ranks of documents citing it. In addition, the rank of a document is calculated from a constant representing the probability that a browser through the database will randomly jump to the document. The method is particularly useful in enhancing the performance of search engine results for hypermedia databases, such as the world wide web, whose documents have a large variation in quality.

entry:
@Misc{Page:2001:MNR,
  author =       "Lawrence Page",
  title =        "Method for node ranking in a linked database",
  howpublished = "US Patent 6,285,999",
  day =          "4",
  month =        sep,
  year =         "2001",
  bibdate =      "Thu Jun 02 08:24:11 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "Filed January 9, 1998. Expires around January 9,
                 2018.",
  URL =          "http://patft.uspto.gov/netahtml/PTO/srchnum.htm",
  abstract =     "A method assigns importance ranks to nodes in a linked
                 database, such as any database of documents containing
                 citations, the world wide web or any other hypermedia
                 database. The rank assigned to a document is calculated
                 from the ranks of documents citing it. In addition, the
                 rank of a document is calculated from a constant
                 representing the probability that a browser through the
                 database will randomly jump to the document. The method
                 is particularly useful in enhancing the performance of
                 search engine results for hypermedia databases, such as
                 the world wide web, whose documents have a large
                 variation in quality.",
  acknowledgement = ack-nhfb,
  remark =       "This may be the main patent behind the Google search
                 engine.",
}


bibtype: inproceedings
filename: pagerank.bib
label: Arasu:2002:PCS
author: Arvind Arasu and Jasmine Novak and Andrew Tomkins and John Tomlin
title: PageRank Computation and the Structure of the Web: Experiments and Algorithms
crossref: Anonymous:2002:PIW
pages: ??--??
year: 2002
bibdate: Thu Oct 24 15:18:39 2002
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www2002.org/CDROM/
bibtimestamp: 2002.10.24 15:18:39 ???
URL: https://www.math.utah.edu/pub/tex/bib/master.bib; http://www2002.org/CDROM/poster/173.pdf
acknowledgement: ack-nhfb
annote: PageRank is the Google search algorithm.
rawauthor: Arvind Arasu and Jasmine Novak and Andrew Tomkins and John Tomlin
rawtitle: {PageRank} Computation and the Structure of the {Web}: Experiments and Algorithms
entry:
@InProceedings{Arasu:2002:PCS,
  author =       "Arvind Arasu and Jasmine Novak and Andrew Tomkins and
                 John Tomlin",
  title =        "{PageRank} Computation and the Structure of the {Web}:
                 Experiments and Algorithms",
  crossref =     "Anonymous:2002:PIW",
  pages =        "??--??",
  year =         "2002",
  bibdate =      "Thu Oct 24 15:18:39 2002",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www2002.org/CDROM/",
  URL =          "https://www.math.utah.edu/pub/tex/bib/master.bib;
                 http://www2002.org/CDROM/poster/173.pdf",
  acknowledgement = ack-nhfb,
  annote =       "PageRank is the Google search algorithm.",
  pagecount =    "5",
}


bibtype: inproceedings
filename: pagerank.bib
label: Chen:2002:ETC
author: Yen-Yu Chen and Qingqing Gan and Torsten Suel
editor: ACM
title: I/O-efficient techniques for computing PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 549--557
year: 2002
DOI: https://doi.org/10.1145/238386.238450
ISBN: 1-58113-492-4 OR 1581134924
ISBN13: 978-1-58113-492-6 OR 9781581134926
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: external memory algorithms; link-based ranking; out-of-core; pagerank; search engines
acknowledgement: ack-nhfb
rawauthor: Yen-Yu Chen and Qingqing Gan and Torsten Suel
raweditor: {ACM}
rawtitle: {I/O}-efficient techniques for computing {PageRank}
booktitle:
Conference on Information and Knowledge Management Proceedings of the eleventh international conference on Information and knowledge management

abstract:
Over the last few years, most major search engines have integrated link-based ranking techniques in order to provide more accurate search results. One widely known approach is the Pagerank technique, which forms the basis of the Google ranking scheme, and which assigns a global importance measure to each page based on the importance of other pages pointing to it. The main advantage of the Pagerank measure is that it is independent of the query posed by a user; this means that it can be precomputed and then used to optimize the layout of the inverted index structure accordingly. However, computing the Pagerank measure requires implementing an iterative process on a massive graph corresponding to billions of web pages and hyperlinks.In this paper, we study I/O-efficient techniques to perform this iterative computation. We derive two algorithms for Pagerank based on techniques proposed for out-of-core graph algorithms, and compare them to two existing algorithms proposed by Haveliwala. We also consider the implementation of a recently proposed topic-sensitive version of Pagerank. Our experimental results show that for very large data sets, significant improvements over previous results can be achieved on machines with moderate amounts of memory. On the other hand, at most minor improvements are possible on data sets that are only moderately larger than memory, which is the case in many practical scenarios.

rawabstract:
Over the last few years, most major search engines have integrated link-based ranking techniques in order to provide more accurate search results. One widely known approach is the Pagerank technique, which forms the basis of the Google ranking scheme, and which assigns a global importance measure to each page based on the importance of other pages pointing to it. The main advantage of the Pagerank measure is that it is independent of the query posed by a user; this means that it can be precomputed and then used to optimize the layout of the inverted index structure accordingly. However, computing the Pagerank measure requires implementing an iterative process on a massive graph corresponding to billions of web pages and hyperlinks.In this paper, we study I/O-efficient techniques to perform this iterative computation. We derive two algorithms for Pagerank based on techniques proposed for out-of-core graph algorithms, and compare them to two existing algorithms proposed by Haveliwala. We also consider the implementation of a recently proposed topic-sensitive version of Pagerank. Our experimental results show that for very large data sets, significant improvements over previous results can be achieved on machines with moderate amounts of memory. On the other hand, at most minor improvements are possible on data sets that are only moderately larger than memory, which is the case in many practical scenarios.

rawbooktitle:
Conference on Information and Knowledge Management Proceedings of the eleventh international conference on Information and knowledge management

entry:
@InProceedings{Chen:2002:ETC,
  author =       "Yen-Yu Chen and Qingqing Gan and Torsten Suel",
  editor =       "{ACM}",
  booktitle =    "Conference on Information and Knowledge Management
                 Proceedings of the eleventh international conference on
                 Information and knowledge management",
  title =        "{I/O}-efficient techniques for computing {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "549--557",
  year =         "2002",
  DOI =          "https://doi.org/10.1145/238386.238450",
  ISBN =         "1-58113-492-4",
  ISBN-13 =      "978-1-58113-492-6",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Over the last few years, most major search engines
                 have integrated link-based ranking techniques in order
                 to provide more accurate search results. One widely
                 known approach is the Pagerank technique, which forms
                 the basis of the Google ranking scheme, and which
                 assigns a global importance measure to each page based
                 on the importance of other pages pointing to it. The
                 main advantage of the Pagerank measure is that it is
                 independent of the query posed by a user; this means
                 that it can be precomputed and then used to optimize
                 the layout of the inverted index structure accordingly.
                 However, computing the Pagerank measure requires
                 implementing an iterative process on a massive graph
                 corresponding to billions of web pages and
                 hyperlinks.In this paper, we study I/O-efficient
                 techniques to perform this iterative computation. We
                 derive two algorithms for Pagerank based on techniques
                 proposed for out-of-core graph algorithms, and compare
                 them to two existing algorithms proposed by Haveliwala.
                 We also consider the implementation of a recently
                 proposed topic-sensitive version of Pagerank. Our
                 experimental results show that for very large data
                 sets, significant improvements over previous results
                 can be achieved on machines with moderate amounts of
                 memory. On the other hand, at most minor improvements
                 are possible on data sets that are only moderately
                 larger than memory, which is the case in many practical
                 scenarios.",
  acknowledgement = ack-nhfb,
  keywords =     "external memory algorithms; link-based ranking;
                 out-of-core; pagerank; search engines",
}


bibtype: inproceedings
filename: pagerank.bib
label: Chen:2002:UFW
author: Zheng Chen and Li Tao and Jidong Wang and Liu Wenyin and Wei-Ying Ma
title: A unified framework for Web link analysis
crossref: WangLing:2002:PTI
pages: 63--70
year: 2002
DOI: https://doi.org/10.1109/WISE.2002.1181644
bibdate: Thu May 06 14:00:37 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 14:00:37 ???
acknowledgement: ack-nhfb
rawauthor: Zheng Chen and Li Tao and Jidong Wang and Liu Wenyin and Wei-Ying Ma
rawtitle: A unified framework for {Web} link analysis
entry:
@InProceedings{Chen:2002:UFW,
  author =       "Zheng Chen and Li Tao and Jidong Wang and Liu Wenyin
                 and Wei-Ying Ma",
  title =        "A unified framework for {Web} link analysis",
  crossref =     "WangLing:2002:PTI",
  pages =        "63--70",
  year =         "2002",
  DOI =          "https://doi.org/10.1109/WISE.2002.1181644",
  bibdate =      "Thu May 06 14:00:37 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: techreport
filename: pagerank.bib
label: Colley:2002:CBF
author: W. N. Colley
title: Colley's Bias Free College Football Ranking Method: The Colley Matrix Explained
type: Technical Report
institution: Princeton University
address: Princeton, NJ, USA
pages: 63--70
year: 2002
bibdate: Tue Aug 11 16:32:30 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:32:30 ???
URL: http://www.colleyrankings.com/matrate.pdf
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: W. N. Colley
rawtitle: {Colley}'s Bias Free College Football Ranking Method: The {Colley} Matrix Explained
entry:
@TechReport{Colley:2002:CBF,
  author =       "W. N. Colley",
  title =        "{Colley}'s Bias Free College Football Ranking Method:
                 The {Colley} Matrix Explained",
  type =         "Technical Report",
  institution =  "Princeton University",
  address =      "Princeton, NJ, USA",
  year =         "2002",
  bibdate =      "Tue Aug 11 16:32:30 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.colleyrankings.com/matrate.pdf",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: article
filename: pagerank.bib
label: Dhyani:2002:SWM
author: Devanshu Dhyani and Wee Keong Ng and Sourav S. Bhowmick
title: A survey of Web metrics
journal: j-COMP-SURV
volume: 34
number: 4
pages: 469--503
month: dec
monthnumber: 12
year: 2002
CODEN: CMSVAN
DOI: https://doi.org/10.1145/592642.592645
ISSN: 0360-0300 (print), 1557-7341 (electronic) OR 03600300 (print), 15577341 (electronic)
ISSNL: 0360-0300 OR 03600300
bibdate: Thu Jun 19 10:18:33 MDT 2008
bibtimestamp: 2008.06.19 10:18:33 MDT
URL: https://www.math.utah.edu/pub/tex/bib/compsurv.bib
fjournal: ACM Computing Surveys
keywords: Information theoretic; PageRank; quality metrics; Web graph; Web metrics; Web page similarity
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J204
rawauthor: Devanshu Dhyani and Wee Keong Ng and Sourav S. Bhowmick
rawtitle: A survey of {Web} metrics
bibsource:
http://www.acm.org/pubs/contents/journals/surveys/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

abstract:
The unabated growth and increasing significance of the World Wide Web has resulted in a flurry of research activity to improve its capacity for serving information more effectively. But at the heart of these efforts lie implicit assumptions about `quality' and `usefulness' of Web resources and services. This observation points towards measurements and models that quantify various attributes of web sites. The science of measuring all aspects of information, especially its storage and retrieval or informetrics has interested information scientists for decades before the existence of the Web. Is Web informetrics any different, or is it just an application of classical informetrics to a new medium? In this article, we examine this issue by classifying and discussing a wide ranging set of Web metrics. We present the origins, measurement functions, formulations and comparisons of well-known Web metrics for quantifying Web graph properties, Web page significance, Web page similarity, search and retrieval, usage characterization and information theoretic properties. We also discuss how these metrics can be applied for improving Web information access and use.

rawabstract:
The unabated growth and increasing significance of the World Wide Web has resulted in a flurry of research activity to improve its capacity for serving information more effectively. But at the heart of these efforts lie implicit assumptions about `quality' and `usefulness' of Web resources and services. This observation points towards measurements and models that quantify various attributes of web sites. The science of measuring all aspects of information, especially its storage and retrieval or informetrics has interested information scientists for decades before the existence of the Web. Is Web informetrics any different, or is it just an application of classical informetrics to a new medium? In this article, we examine this issue by classifying and discussing a wide ranging set of Web metrics. We present the origins, measurement functions, formulations and comparisons of well-known Web metrics for quantifying Web graph properties, Web page significance, Web page similarity, search and retrieval, usage characterization and information theoretic properties. We also discuss how these metrics can be applied for improving Web information access and use.

entry:
@Article{Dhyani:2002:SWM,
  author =       "Devanshu Dhyani and Wee Keong Ng and Sourav S.
                 Bhowmick",
  title =        "A survey of {Web} metrics",
  journal =      j-COMP-SURV,
  volume =       "34",
  number =       "4",
  pages =        "469--503",
  month =        dec,
  year =         "2002",
  CODEN =        "CMSVAN",
  DOI =          "https://doi.org/10.1145/592642.592645",
  ISSN =         "0360-0300 (print), 1557-7341 (electronic)",
  ISSN-L =       "0360-0300",
  bibdate =      "Thu Jun 19 10:18:33 MDT 2008",
  bibsource =    "http://www.acm.org/pubs/contents/journals/surveys/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://www.math.utah.edu/pub/tex/bib/compsurv.bib",
  abstract =     "The unabated growth and increasing significance of the
                 World Wide Web has resulted in a flurry of research
                 activity to improve its capacity for serving
                 information more effectively. But at the heart of these
                 efforts lie implicit assumptions about `quality' and
                 `usefulness' of Web resources and services. This
                 observation points towards measurements and models that
                 quantify various attributes of web sites. The science
                 of measuring all aspects of information, especially its
                 storage and retrieval or informetrics has interested
                 information scientists for decades before the existence
                 of the Web. Is Web informetrics any different, or is it
                 just an application of classical informetrics to a new
                 medium? In this article, we examine this issue by
                 classifying and discussing a wide ranging set of Web
                 metrics. We present the origins, measurement functions,
                 formulations and comparisons of well-known Web metrics
                 for quantifying Web graph properties, Web page
                 significance, Web page similarity, search and
                 retrieval, usage characterization and information
                 theoretic properties. We also discuss how these metrics
                 can be applied for improving Web information access and
                 use.",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM Computing Surveys",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J204",
  keywords =     "Information theoretic; PageRank; quality metrics; Web
                 graph; Web metrics; Web page similarity",
}


bibtype: inproceedings
filename: pagerank.bib
label: Ding:2002:PHU
author: Chris Ding and Xiaofeng He and Parry Husbands and Hongyuan Zha and Horst D. Simon
editor: ACM
title: PageRank, HITS and a unified framework for link analysis
publisher: pub-ACM
address: pub-ACM:adr
pages: 353--354
year: 2002
DOI: https://doi.org/10.1145/324133.324140
ISBN: 1-58113-561-0 OR 1581135610
ISBN13: 978-1-58113-561-9 OR 9781581135619
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
acknowledgement: ack-nhfb
rawauthor: Chris Ding and Xiaofeng He and Parry Husbands and Hongyuan Zha and Horst D. Simon
raweditor: {ACM}
rawtitle: {PageRank}, {HITS} and a unified framework for link analysis
booktitle:
Annual ACM Conference on Research and Development in Information Retrieval Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval

abstract:
Two popular link-based webpage ranking algorithms are (i) PageRank[1] and (ii) HITS (Hypertext Induced Topic Selection)[3]. HITS makes the crucial distinction of hubs and authorities and computes them in a mutually reinforcing way. PageRank considers the hyperlink weight normalization and the equilibrium distribution of random surfers as the citation score. We generalize and combine these key concepts into a unified framework, in which we prove that rankings produced by PageRank and HITS are both highly correlated with the ranking by in-degree and out-degree.

rawabstract:
Two popular link-based webpage ranking algorithms are (i) PageRank[1] and (ii) HITS (Hypertext Induced Topic Selection)[3]. HITS makes the crucial distinction of hubs and authorities and computes them in a mutually reinforcing way. PageRank considers the hyperlink weight normalization and the equilibrium distribution of random surfers as the citation score. We generalize and combine these key concepts into a unified framework, in which we prove that rankings produced by PageRank and HITS are both highly correlated with the ranking by in-degree and out-degree.

rawbooktitle:
Annual ACM Conference on Research and Development in Information Retrieval Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval

entry:
@InProceedings{Ding:2002:PHU,
  author =       "Chris Ding and Xiaofeng He and Parry Husbands and
                 Hongyuan Zha and Horst D. Simon",
  editor =       "{ACM}",
  booktitle =    "Annual ACM Conference on Research and Development in
                 Information Retrieval Proceedings of the 25th annual
                 international ACM SIGIR conference on Research and
                 development in information retrieval",
  title =        "{PageRank}, {HITS} and a unified framework for link
                 analysis",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "353--354",
  year =         "2002",
  DOI =          "https://doi.org/10.1145/324133.324140",
  ISBN =         "1-58113-561-0",
  ISBN-13 =      "978-1-58113-561-9",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Two popular link-based webpage ranking algorithms are
                 (i) PageRank[1] and (ii) HITS (Hypertext Induced Topic
                 Selection)[3]. HITS makes the crucial distinction of
                 hubs and authorities and computes them in a mutually
                 reinforcing way. PageRank considers the hyperlink
                 weight normalization and the equilibrium distribution
                 of random surfers as the citation score. We generalize
                 and combine these key concepts into a unified
                 framework, in which we prove that rankings produced by
                 PageRank and HITS are both highly correlated with the
                 ranking by in-degree and out-degree.",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Haveliwala:2002:TSP
author: Taher H. Haveliwala
editor: ACM
title: Topic-sensitive PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 517--526
year: 2002
DOI: https://doi.org/10.1145/511446.511513
ISBN: 1-58113-449-5 OR 1581134495
ISBN13: 978-1-58113-449-0 OR 9781581134490
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: link structure; PageRank; personalized search; search; search in context; web graph
acknowledgement: ack-nhfb
rawauthor: Taher H. Haveliwala
raweditor: {ACM}
rawtitle: Topic-sensitive {PageRank}
booktitle:
International World Wide Web Conference Proceedings of the 11th international conference on World Wide Web

abstract:
In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative 'importance' of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.

rawabstract:
In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative 'importance' of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.

rawbooktitle:
International World Wide Web Conference Proceedings of the 11th international conference on World Wide Web

entry:
@InProceedings{Haveliwala:2002:TSP,
  author =       "Taher H. Haveliwala",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 11th international conference on World Wide Web",
  title =        "Topic-sensitive {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "517--526",
  year =         "2002",
  DOI =          "https://doi.org/10.1145/511446.511513",
  ISBN =         "1-58113-449-5",
  ISBN-13 =      "978-1-58113-449-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In the original PageRank algorithm for improving the
                 ranking of search-query results, a single PageRank
                 vector is computed, using the link structure of the
                 Web, to capture the relative 'importance' of Web pages,
                 independent of any particular search query. To yield
                 more accurate search results, we propose computing a
                 set of PageRank vectors, biased using a set of
                 representative topics, to capture more accurately the
                 notion of importance with respect to a particular
                 topic. By using these (precomputed) biased PageRank
                 vectors to generate query-specific importance scores
                 for pages at query time, we show that we can generate
                 more accurate rankings than with a single, generic
                 PageRank vector. For ordinary keyword search queries,
                 we compute the topic-sensitive PageRank scores for
                 pages satisfying the query using the topic of the query
                 keywords. For searches done in context (e.g., when the
                 search query is performed by highlighting words in a
                 Web page), we compute the topic-sensitive PageRank
                 scores using the topic of the context in which the
                 query appeared.",
  acknowledgement = ack-nhfb,
  keywords =     "link structure; PageRank; personalized search; search;
                 search in context; web graph",
}


bibtype: inproceedings
filename: pagerank.bib
label: Jeh:2002:SMS
author: Glen Jeh and Jennifer Widom
title: SimRank: A measure of structural-context similarity
publisher: pub-ACM
address: pub-ACM:adr
pages: 538--543
year: 2002
DOI: https://doi.org/10.1145/775047.775126
ISBN: 1-58113-567-X OR 158113567X
ISBN13: 978-1-58113-567-1 OR 9781581135671
LCCN: QA76.9.D3 I58 2002
bibdate: Tue Aug 11 17:08:35 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:08:35 ???
acknowledgement: ack-nhfb
bookpages: xiv + 704
rawauthor: Glen Jeh and Jennifer Widom
rawtitle: {SimRank}: A measure of structural-context similarity
booktitle:
Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'02: July 23--36, 2002, Edmonton, Alberta, Canada

rawbooktitle:
{Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'02: July 23--36, 2002, Edmonton, Alberta, Canada}

entry:
@InProceedings{Jeh:2002:SMS,
  author =       "Glen Jeh and Jennifer Widom",
  booktitle =    "{Proceedings of the Eighth ACM SIGKDD International
                 Conference on Knowledge Discovery and Data Mining,
                 KDD'02: July 23--36, 2002, Edmonton, Alberta, Canada}",
  title =        "{SimRank}: A measure of structural-context
                 similarity",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "538--543",
  year =         "2002",
  DOI =          "https://doi.org/10.1145/775047.775126",
  ISBN =         "1-58113-567-X",
  ISBN-13 =      "978-1-58113-567-1",
  LCCN =         "QA76.9.D3 I58 2002",
  bibdate =      "Tue Aug 11 17:08:35 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "xiv + 704",
}


bibtype: article
filename: pagerank.bib
label: Kim:2002:ICP
author: Sung Jin Kim and Sang Ho Lee
title: An Improved Computation of the PageRank Algorithm
journal: j-LECT-NOTES-COMP-SCI
volume: 2291
pages: 73--85
year: 2002
CODEN: LNCSD9
ISBN: 3-540-43343-0 OR 3540433430
ISBN13: 978-3-540-43343-9 OR 9783540433439
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
bibdate: Sat May 8 18:33:10 MDT 2010
bibtimestamp: 2010.05.08 18:33:10 MDT
fjournal: Lecture Notes in Computer Science
ZMnumber: 1056.68526
acknowledgement: ack-nhfb
rawauthor: Sung Jin Kim and Sang Ho Lee
rawtitle: An Improved Computation of the {PageRank} Algorithm
bibsource:
http://link.springer-ny.com/link/service/series/0558/tocs/t2291.htm; https://www.math.utah.edu/pub/tex/bib/lncs2002a.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
http://link.springer-ny.com/link/service/series/0558/bibs/2291/22910073.htm; http://link.springer-ny.com/link/service/series/0558/papers/2291/22910073.pdf

entry:
@Article{Kim:2002:ICP,
  author =       "Sung Jin Kim and Sang Ho Lee",
  title =        "An Improved Computation of the {PageRank} Algorithm",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "2291",
  pages =        "73--85",
  year =         "2002",
  CODEN =        "LNCSD9",
  ISBN =         "3-540-43343-0",
  ISBN-13 =      "978-3-540-43343-9",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://link.springer-ny.com/link/service/series/0558/tocs/t2291.htm;
                 https://www.math.utah.edu/pub/tex/bib/lncs2002a.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer-ny.com/link/service/series/0558/bibs/2291/22910073.htm;
                 http://link.springer-ny.com/link/service/series/0558/papers/2291/22910073.pdf",
  ZMnumber =     "1056.68526",
  acknowledgement = ack-nhfb,
  fjournal =     "Lecture Notes in Computer Science",
}


bibtype: techreport
filename: pagerank.bib
label: Moler:2002:CCW
author: Cleve B. Moler
type: Technical note
institution: inst-MATHWORKS
address: inst-MATHWORKS:adr
pages: 1
month: oct
monthnumber: 10
year: 2002
bibdate: Thu Oct 24 07:16:21 2002
bibtimestamp: 2002.10.24 07:16:21 ???
URL: http://www.mathworks.com/company/newsletter/clevescorner/oct02_cleve.shtml
keywords: Matlab
acknowledgement: ack-nhfb
rawauthor: Cleve B. Moler
title:
Cleve's Corner: The World's Largest Matrix Computation: Google's PageRank is an eigenvector of a matrix of order $ 2.7 $ billion

bibsource:
https://www.math.utah.edu/pub/bibnet/authors/m/moler-cleve-b.bib; https://www.math.utah.edu/pub/tex/bib/matlab.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
{Cleve}'s Corner: The World's Largest Matrix Computation: {Google}'s {PageRank} is an eigenvector of a matrix of order $ 2.7 $ billion

entry:
@TechReport{Moler:2002:CCW,
  author =       "Cleve B. Moler",
  title =        "{Cleve}'s Corner: The World's Largest Matrix
                 Computation: {Google}'s {PageRank} is an eigenvector of
                 a matrix of order $ 2.7 $ billion",
  type =         "Technical note",
  institution =  inst-MATHWORKS,
  address =      inst-MATHWORKS:adr,
  pages =        "1",
  month =        oct,
  year =         "2002",
  bibdate =      "Thu Oct 24 07:16:21 2002",
  bibsource =    "https://www.math.utah.edu/pub/bibnet/authors/m/moler-cleve-b.bib;
                 https://www.math.utah.edu/pub/tex/bib/matlab.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.mathworks.com/company/newsletter/clevescorner/oct02_cleve.shtml",
  acknowledgement = ack-nhfb,
  keywords =     "Matlab",
}


bibtype: article
filename: pagerank.bib
label: Pandurangan:2002:UPC
author: Gopal Pandurangan and Prabhakar Raghavan and Eli Upfal
title: Using PageRank to characterize Web structure
journal: j-LECT-NOTES-COMP-SCI
volume: 2387
pages: 330--339
year: 2002
CODEN: LNCSD9
DOI: https://doi.org/10.1007/3-540-45655-4_36
ISBN: 3-540-43996-X OR 354043996X
ISBN13: 978-3-540-43996-7 OR 9783540439967
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
MRclass: 68M10 68U35
MRnumber: MR2064528
bibdate: Tue Sep 10 19:10:08 MDT 2002
bibtimestamp: 2002.09.10 19:10:08 MDT
series: ser-LNCS
fjournal: Lecture Notes in Computer Science
ZMnumber: 1077.68527
acknowledgement: ack-nhfb
rawauthor: Gopal Pandurangan and Prabhakar Raghavan and Eli Upfal
rawtitle: Using {PageRank} to characterize {Web} structure
bibsource:
http://link.springer-ny.com/link/service/series/0558/tocs/t2387.htm; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
http://link.springer-ny.com/link/service/series/0558/bibs/2387/23870330.htm; http://link.springer-ny.com/link/service/series/0558/papers/2387/23870330.pdf

entry:
@Article{Pandurangan:2002:UPC,
  author =       "Gopal Pandurangan and Prabhakar Raghavan and Eli
                 Upfal",
  title =        "Using {PageRank} to characterize {Web} structure",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "2387",
  pages =        "330--339",
  year =         "2002",
  CODEN =        "LNCSD9",
  DOI =          "https://doi.org/10.1007/3-540-45655-4_36",
  ISBN =         "3-540-43996-X",
  ISBN-13 =      "978-3-540-43996-7",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  LCCN =         "????",
  MRclass =      "68M10 68U35",
  MRnumber =     "MR2064528",
  bibdate =      "Tue Sep 10 19:10:08 MDT 2002",
  bibsource =    "http://link.springer-ny.com/link/service/series/0558/tocs/t2387.htm;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  URL =          "http://link.springer-ny.com/link/service/series/0558/bibs/2387/23870330.htm;
                 http://link.springer-ny.com/link/service/series/0558/papers/2387/23870330.pdf",
  ZMnumber =     "1077.68527",
  acknowledgement = ack-nhfb,
  fjournal =     "Lecture Notes in Computer Science",
}


bibtype: article
filename: pagerank.bib
label: Pretto:2002:TAG
author: Luca Pretto
title: A Theoretical Analysis of Google's PageRank
journal: j-LECT-NOTES-COMP-SCI
volume: 2476
pages: 131--144
year: 2002
CODEN: LNCSD9
ISBN: 3-540-44158-1 OR 3540441581
ISBN13: 978-3-540-44158-8 OR 9783540441588
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
bibdate: Sat Nov 30 20:57:37 MST 2002
bibtimestamp: 2002.11.30 20:57:37 MST
fjournal: Lecture Notes in Computer Science
acknowledgement: ack-nhfb
rawauthor: Luca Pretto
rawtitle: A Theoretical Analysis of {Google}'s {PageRank}
bibsource:
http://link.springer-ny.com/link/service/series/0558/tocs/t2476.htm; https://www.math.utah.edu/pub/tex/bib/lncs2002e.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
http://link.springer.de/link/service/series/0558/bibs/2476/24760131.htm; http://link.springer.de/link/service/series/0558/papers/2476/24760131.pdf

entry:
@Article{Pretto:2002:TAG,
  author =       "Luca Pretto",
  title =        "A Theoretical Analysis of {Google}'s {PageRank}",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "2476",
  pages =        "131--144",
  year =         "2002",
  CODEN =        "LNCSD9",
  ISBN =         "3-540-44158-1",
  ISBN-13 =      "978-3-540-44158-8",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  LCCN =         "????",
  bibdate =      "Sat Nov 30 20:57:37 MST 2002",
  bibsource =    "http://link.springer-ny.com/link/service/series/0558/tocs/t2476.htm;
                 https://www.math.utah.edu/pub/tex/bib/lncs2002e.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.de/link/service/series/0558/bibs/2476/24760131.htm;
                 http://link.springer.de/link/service/series/0558/papers/2476/24760131.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "Lecture Notes in Computer Science",
}


bibtype: book
filename: pagerank.bib
label: Baldi:2003:MIW
author: Pierre Baldi and Paolo Frasconi and Padhraic Smyth
title: Modeling the Internet and the Web: probabilistic methods and algorithms
publisher: pub-WILEY
address: pub-WILEY:adr
pages: xix + 285
year: 2003
ISBN: 0-470-86492-3 (e-book), 0-470-84906-1 OR 0470864923 (ebook), 0470849061
ISBN13: 978-0-470-86492-0 (e-book), 978-0-470-84906-4 OR 9780470864920 (ebook), 9780470849064
LCCN: TK5105.875.I57 B35 2003eb
bibdate: Fri Jun 3 10:03:23 MDT 2011
bibsource: fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 10:03:23 MDT
acknowledgement: ack-nhfb
rawauthor: Pierre Baldi and Paolo Frasconi and Padhraic Smyth
rawtitle: Modeling the {Internet} and the {Web}: probabilistic methods and algorithms
subject:
Internet; Mathematical models; Telecommunication; Traffic; World Wide Web; Cyberspace; Probabilities; Computers; Web; General; Networking; Intranets and Extranets

TOC:
Mathematical Background Probability and Learning from a Bayesian Perspective Parameter Estimation from Data Basic principles A simple die example Mixture Models and the Expectation Maximization Algorithm Graphical Models Bayesian networks Belief propagation Learning directed graphical models from data Classification Clustering Power-Law Distributions Scale-free properties (80/20 rule) Applications to Languages: Zipf's and Heaps' Laws Origin of power-law distributions and Fermi's model Basic WWW Technologies Web Documents SGML and HTML General structure of an HTML document Links Resource Identifiers: URI, URL, and URN Protocols Reference models and TCP/IP The domain name system The Hypertext Transfer Protocol Programming examples Log Files Search Engines Coverage Basic crawling Web Graphs Internet and Web Graphs Power-law size Power-law connectivity Small-world networks Power law of PageRank The bow-tie structure Generative Models for the Web Graph and Other Networks Web page growth Lattice perturbation models: between order and disorder Preferential attachment models, or the rich get richer Copy models PageRank models Applications Distributed search algorithms Subgraph patterns and communities Robustness and vulnerability Notes and Additional Technical References Text Analysis Indexing Compression techniques Lexical Processing Tokenization

rawTOC:
Mathematical Background \\ Probability and Learning from a Bayesian Perspective \\ Parameter Estimation from Data \\ Basic principles \\ A simple die example \\ Mixture Models and the Expectation Maximization Algorithm \\ Graphical Models \\ Bayesian networks \\ Belief propagation \\ Learning directed graphical models from data \\ Classification \\ Clustering \\ Power-Law Distributions \\ Scale-free properties (80/20 rule) \\ Applications to Languages: Zipf's and Heaps' Laws \\ Origin of power-law distributions and Fermi's model \\ Basic WWW Technologies \\ Web Documents \\ SGML and HTML \\ General structure of an HTML document \\ Links \\ Resource Identifiers: URI, URL, and URN \\ Protocols \\ Reference models and TCP/IP \\ The domain name system \\ The Hypertext Transfer Protocol \\ Programming examples \\ Log Files \\ Search Engines \\ Coverage \\ Basic crawling \\ Web Graphs \\ Internet and Web Graphs \\ Power-law size \\ Power-law connectivity \\ Small-world networks \\ Power law of PageRank \\ The bow-tie structure \\ Generative Models for the Web Graph and Other Networks \\ Web page growth \\ Lattice perturbation models: between order and disorder \\ Preferential attachment models, or the rich get richer \\ Copy models \\ PageRank models \\ Applications \\ Distributed search algorithms \\ Subgraph patterns and communities \\ Robustness and vulnerability \\ Notes and Additional Technical References \\ Text Analysis \\ Indexing \\ Compression techniques \\ Lexical Processing \\ Tokenization

entry:
@Book{Baldi:2003:MIW,
  author =       "Pierre Baldi and Paolo Frasconi and Padhraic Smyth",
  title =        "Modeling the {Internet} and the {Web}: probabilistic
                 methods and algorithms",
  publisher =    pub-WILEY,
  address =      pub-WILEY:adr,
  pages =        "xix + 285",
  year =         "2003",
  ISBN =         "0-470-86492-3 (e-book), 0-470-84906-1",
  ISBN-13 =      "978-0-470-86492-0 (e-book), 978-0-470-84906-4",
  LCCN =         "TK5105.875.I57 B35 2003eb",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  subject =      "Internet; Mathematical models; Telecommunication;
                 Traffic; World Wide Web; Cyberspace; Probabilities;
                 Computers; Web; General; Networking; Intranets and
                 Extranets",
  tableofcontents = "Mathematical Background \\
                 Probability and Learning from a Bayesian Perspective
                 \\
                 Parameter Estimation from Data \\
                 Basic principles \\
                 A simple die example \\
                 Mixture Models and the Expectation Maximization
                 Algorithm \\
                 Graphical Models \\
                 Bayesian networks \\
                 Belief propagation \\
                 Learning directed graphical models from data \\
                 Classification \\
                 Clustering \\
                 Power-Law Distributions \\
                 Scale-free properties (80/20 rule) \\
                 Applications to Languages: Zipf's and Heaps' Laws \\
                 Origin of power-law distributions and Fermi's model \\
                 Basic WWW Technologies \\
                 Web Documents \\
                 SGML and HTML \\
                 General structure of an HTML document \\
                 Links \\
                 Resource Identifiers: URI, URL, and URN \\
                 Protocols \\
                 Reference models and TCP/IP \\
                 The domain name system \\
                 The Hypertext Transfer Protocol \\
                 Programming examples \\
                 Log Files \\
                 Search Engines \\
                 Coverage \\
                 Basic crawling \\
                 Web Graphs \\
                 Internet and Web Graphs \\
                 Power-law size \\
                 Power-law connectivity \\
                 Small-world networks \\
                 Power law of PageRank \\
                 The bow-tie structure \\
                 Generative Models for the Web Graph and Other Networks
                 \\
                 Web page growth \\
                 Lattice perturbation models: between order and disorder
                 \\
                 Preferential attachment models, or the rich get richer
                 \\
                 Copy models \\
                 PageRank models \\
                 Applications \\
                 Distributed search algorithms \\
                 Subgraph patterns and communities \\
                 Robustness and vulnerability \\
                 Notes and Additional Technical References \\
                 Text Analysis \\
                 Indexing \\
                 Compression techniques \\
                 Lexical Processing \\
                 Tokenization",
}


bibtype: inproceedings
filename: pagerank.bib
label: Bianchini:2003:PWC
author: M. Bianchini and M. Gori and F. Scarselli
title: PageRank and Web communities
crossref: Liu:2003:ISW
pages: 365--371
year: 2003
DOI: https://doi.org/10.1109/WI.2003.1241217
bibdate: Fri Feb 19 18:30:00 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 18:30:00 ???
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1241217
acknowledgement: ack-nhfb
rawauthor: M. Bianchini and M. Gori and F. Scarselli
rawtitle: {PageRank} and {Web} communities
abstract:
The definition of the ordering of the Web pages, returned on a given query, is a crucial topic, which gives rise to the notion of Web visibility. A fundamental contribution towards the conception of appropriate ordering criteria has been given by means of the introduction of PageRank, which takes into account only the hyper-linked structure of the Web, regardless of the content of the pages. In this paper, we introduce a circuit analysis which allows us to understand the distribution of PageRank, and show some basic results for understanding the way it migrates amongst communities. In particular, we highlight some topological properties which suggest methods for the promotion of Web communities. These results confirm the importance and the effectiveness of PageRank for discovering relevant information but, at the same time, point out its vulnerability to spamming.

rawabstract:
The definition of the ordering of the Web pages, returned on a given query, is a crucial topic, which gives rise to the notion of Web visibility. A fundamental contribution towards the conception of appropriate ordering criteria has been given by means of the introduction of PageRank, which takes into account only the hyper-linked structure of the Web, regardless of the content of the pages. In this paper, we introduce a circuit analysis which allows us to understand the distribution of PageRank, and show some basic results for understanding the way it migrates amongst communities. In particular, we highlight some topological properties which suggest methods for the promotion of Web communities. These results confirm the importance and the effectiveness of PageRank for discovering relevant information but, at the same time, point out its vulnerability to spamming.

entry:
@InProceedings{Bianchini:2003:PWC,
  author =       "M. Bianchini and M. Gori and F. Scarselli",
  title =        "{PageRank} and {Web} communities",
  crossref =     "Liu:2003:ISW",
  pages =        "365--371",
  year =         "2003",
  DOI =          "https://doi.org/10.1109/WI.2003.1241217",
  bibdate =      "Fri Feb 19 18:30:00 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1241217",
  abstract =     "The definition of the ordering of the Web pages,
                 returned on a given query, is a crucial topic, which
                 gives rise to the notion of Web visibility. A
                 fundamental contribution towards the conception of
                 appropriate ordering criteria has been given by means
                 of the introduction of PageRank, which takes into
                 account only the hyper-linked structure of the Web,
                 regardless of the content of the pages. In this paper,
                 we introduce a circuit analysis which allows us to
                 understand the distribution of PageRank, and show some
                 basic results for understanding the way it migrates
                 amongst communities. In particular, we highlight some
                 topological properties which suggest methods for the
                 promotion of Web communities. These results confirm the
                 importance and the effectiveness of PageRank for
                 discovering relevant information but, at the same time,
                 point out its vulnerability to spamming.",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Chirita:2003:FRH
author: P.-A. Chirita and D. Olmedilla and W. Nejdl
title: Finding related hubs and authorities
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 214--215
year: 2003
ISBN: 0-7695-2058-8 OR 0769520588
ISBN13: 978-0-7695-2058-2 OR 9780769520582
bibdate: Mon May 10 12:22:33 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.10 12:22:33 ???
keywords: PageRank
acknowledgement: ack-nhfb
bookpages: xii + 241
rawauthor: P.-A. Chirita and D. Olmedilla and W. Nejdl
rawtitle: Finding related hubs and authorities
booktitle:
First Latin American Web Congress, 2003. LA-WEB 2003, Santiago, Chile, November 10--12, 2003. Proceedings

rawbooktitle:
{First Latin American Web Congress, 2003. LA-WEB 2003, Santiago, Chile, November 10--12, 2003. Proceedings}

entry:
@InProceedings{Chirita:2003:FRH,
  author =       "P.-A. Chirita and D. Olmedilla and W. Nejdl",
  booktitle =    "{First Latin American Web Congress, 2003. LA-WEB 2003,
                 Santiago, Chile, November 10--12, 2003. Proceedings}",
  title =        "Finding related hubs and authorities",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "214--215",
  year =         "2003",
  ISBN =         "0-7695-2058-8",
  ISBN-13 =      "978-0-7695-2058-2",
  LCCN =         "????",
  bibdate =      "Mon May 10 12:22:33 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "xii + 241",
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Ding:2003:PHU
author: C. H. Q. Ding and X. He and P. Husbands and H. Zha and H. D. Simon
title: PageRank: HITS and a unified framework for link analysis
crossref: Barbara:2003:PTS
pages: 353--354
year: 2003
bibdate: Fri Feb 19 15:15:08 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:15:08 ???
acknowledgement: ack-nhfb
rawauthor: C. H. Q. Ding and X. He and P. Husbands and H. Zha and H. D. Simon
rawtitle: {PageRank}: {HITS} and a unified framework for link analysis
entry:
@InProceedings{Ding:2003:PHU,
  author =       "C. H. Q. Ding and X. He and P. Husbands and H. Zha and
                 H. D. Simon",
  title =        "{PageRank}: {HITS} and a unified framework for link
                 analysis",
  crossref =     "Barbara:2003:PTS",
  pages =        "353--354",
  year =         "2003",
  bibdate =      "Fri Feb 19 15:15:08 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: techreport
filename: pagerank.bib
label: Haveliwala:2003:ACA
author: Tamer Haveliwala and Sepandar Kamvar and Glen Jeh
title: An analytical comparison of approaches to personalizing PageRank
type: Technical report
number: 2003-32
institution: Stanford InfoLab, Stanford University
address: Stanford, CA, USA
pages: 4
month: jun
monthnumber: 6
year: 2003
bibdate: Tue Jul 20 16:03:24 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.07.20 16:03:24 ???
URL: http://ilpubs.stanford.edu:8090/596/
acknowledgement: ack-nhfb
rawauthor: Tamer Haveliwala and Sepandar Kamvar and Glen Jeh
rawtitle: An analytical comparison of approaches to personalizing {PageRank}
abstract:
PageRank, the popular link-analysis algorithm for ranking web pages, assigns a query and user independent estimate of ``importance'' to web pages. Query and user sensitive extensions of PageRank, which use a basis set of biased PageRank vectors, have been proposed in order to personalize the ranking function in a tractable way. We analytically compare three recent approaches to personalizing PageRank and discuss the tradeoffs of each one.

rawabstract:
PageRank, the popular link-analysis algorithm for ranking web pages, assigns a query and user independent estimate of ``importance'' to web pages. Query and user sensitive extensions of PageRank, which use a basis set of biased PageRank vectors, have been proposed in order to personalize the ranking function in a tractable way. We analytically compare three recent approaches to personalizing PageRank and discuss the tradeoffs of each one.

entry:
@TechReport{Haveliwala:2003:ACA,
  author =       "Tamer Haveliwala and Sepandar Kamvar and Glen Jeh",
  title =        "An analytical comparison of approaches to
                 personalizing {PageRank}",
  type =         "Technical report",
  number =       "2003-32",
  institution =  "Stanford InfoLab, Stanford University",
  address =      "Stanford, CA, USA",
  pages =        "4",
  month =        jun,
  year =         "2003",
  bibdate =      "Tue Jul 20 16:03:24 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ilpubs.stanford.edu:8090/596/",
  abstract =     "PageRank, the popular link-analysis algorithm for
                 ranking web pages, assigns a query and user independent
                 estimate of ``importance'' to web pages. Query and user
                 sensitive extensions of PageRank, which use a basis set
                 of biased PageRank vectors, have been proposed in order
                 to personalize the ranking function in a tractable way.
                 We analytically compare three recent approaches to
                 personalizing PageRank and discuss the tradeoffs of
                 each one.",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Haveliwala:2003:TSP
author: Taher H. Haveliwala
title: Topic-sensitive PageRank: a context-sensitive ranking algorithm for Web search
journal: j-IEEE-TRANS-KNOWL-DATA-ENG
volume: 15
number: 4
pages: 784--796
month: jul
monthnumber: 7
year: 2003
CODEN: ITKEEH
DOI: https://doi.org/10.1109/TKDE.2003.1208999
ISSN: 1041-4347 OR 10414347
ISSNL: 1041-4347 OR 10414347
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
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1208999
fjournal: IEEE Transactions on Knowledge and Data Engineering
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=27216
journalURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69
rawauthor: Taher H. Haveliwala
rawtitle: Topic-sensitive {PageRank}: a context-sensitive ranking algorithm for {Web} search
abstract:
The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative ``importance'' of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared. By using linear combinations of these (precomputed) biased PageRank vectors to generate context-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. We describe techniques for efficiently implementing a large-scale search system based on the topic-sensitive PageRank scheme.

keywords:
link analysis; PageRank; personalized search; ranking algorithm.; search in context; web graph; Web search

rawabstract:
The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative ``importance'' of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared. By using linear combinations of these (precomputed) biased PageRank vectors to generate context-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. We describe techniques for efficiently implementing a large-scale search system based on the topic-sensitive PageRank scheme.

entry:
@Article{Haveliwala:2003:TSP,
  author =       "Taher H. Haveliwala",
  title =        "Topic-sensitive {PageRank}: a context-sensitive
                 ranking algorithm for {Web} search",
  journal =      j-IEEE-TRANS-KNOWL-DATA-ENG,
  volume =       "15",
  number =       "4",
  pages =        "784--796",
  month =        jul,
  year =         "2003",
  CODEN =        "ITKEEH",
  DOI =          "https://doi.org/10.1109/TKDE.2003.1208999",
  ISSN =         "1041-4347",
  ISSN-L =       "1041-4347",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1208999",
  abstract =     "The original PageRank algorithm for improving the
                 ranking of search-query results computes a single
                 vector, using the link structure of the Web, to capture
                 the relative ``importance'' of Web pages, independent
                 of any particular search query. To yield more accurate
                 search results, we propose computing a set of PageRank
                 vectors, biased using a set of representative topics,
                 to capture more accurately the notion of importance
                 with respect to a particular topic. For ordinary
                 keyword search queries, we compute the topic-sensitive
                 PageRank scores for pages satisfying the query using
                 the topic of the query keywords. For searches done in
                 context (e.g., when the search query is performed by
                 highlighting words in a Web page), we compute the
                 topic-sensitive PageRank scores using the topic of the
                 context in which the query appeared. By using linear
                 combinations of these (precomputed) biased PageRank
                 vectors to generate context-specific importance scores
                 for pages at query time, we show that we can generate
                 more accurate rankings than with a single, generic
                 PageRank vector. We describe techniques for efficiently
                 implementing a large-scale search system based on the
                 topic-sensitive PageRank scheme.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=27216",
  fjournal =     "IEEE Transactions on Knowledge and Data Engineering",
  journal-URL =  "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69",
  keywords =     "link analysis; PageRank; personalized search; ranking
                 algorithm.; search in context; web graph; Web search",
}


bibtype: inproceedings
filename: pagerank.bib
label: Jeh:2003:SPW
author: Glen Jeh and Jennifer Widom
title: Scaling personalized web search
crossref: Hencsey:2003:PTI
pages: 784--796
year: 2003
DOI: https://doi.org/10.1145/775152.775191
bibdate: Mon May 10 14:17:38 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.10 14:17:38 ???
acknowledgement: ack-nhfb
rawauthor: Glen Jeh and Jennifer Widom
rawtitle: Scaling personalized web search
abstract:
Recent web search techniques augment traditional text matching with a global notion of ``importance'' based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased according to a user-specified set of initially-interesting pages. Computing and storing all possible personalized views in advance is impractical, as is computing personalized views at query time, since the computation of each view requires an iterative computation over the web graph. We present new graph-theoretical results, and a new technique based on these results, that encode personalized views as partial vectors. Partial vectors are shared across multiple personalized views, and their computation and storage costs scale well with the number of views. Our approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time. We present efficient dynamic programming algorithms for computing partial vectors, an algorithm for constructing personalized views from partial vectors, and experimental results demonstrating the effectiveness and scalability of our techniques.

rawabstract:
Recent web search techniques augment traditional text matching with a global notion of ``importance'' based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased according to a user-specified set of initially-interesting pages. Computing and storing all possible personalized views in advance is impractical, as is computing personalized views at query time, since the computation of each view requires an iterative computation over the web graph. We present new graph-theoretical results, and a new technique based on these results, that encode personalized views as partial vectors. Partial vectors are shared across multiple personalized views, and their computation and storage costs scale well with the number of views. Our approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time. We present efficient dynamic programming algorithms for computing partial vectors, an algorithm for constructing personalized views from partial vectors, and experimental results demonstrating the effectiveness and scalability of our techniques.

entry:
@InProceedings{Jeh:2003:SPW,
  author =       "Glen Jeh and Jennifer Widom",
  title =        "Scaling personalized web search",
  crossref =     "Hencsey:2003:PTI",
  year =         "2003",
  DOI =          "https://doi.org/10.1145/775152.775191",
  bibdate =      "Mon May 10 14:17:38 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Recent web search techniques augment traditional text
                 matching with a global notion of ``importance'' based
                 on the linkage structure of the web, such as in
                 Google's PageRank algorithm. For more refined searches,
                 this global notion of importance can be specialized to
                 create personalized views of importance--for example,
                 importance scores can be biased according to a
                 user-specified set of initially-interesting pages.
                 Computing and storing all possible personalized views
                 in advance is impractical, as is computing personalized
                 views at query time, since the computation of each view
                 requires an iterative computation over the web graph.
                 We present new graph-theoretical results, and a new
                 technique based on these results, that encode
                 personalized views as partial vectors. Partial vectors
                 are shared across multiple personalized views, and
                 their computation and storage costs scale well with the
                 number of views. Our approach enables incremental
                 computation, so that the construction of personalized
                 views from partial vectors is practical at query time.
                 We present efficient dynamic programming algorithms for
                 computing partial vectors, an algorithm for
                 constructing personalized views from partial vectors,
                 and experimental results demonstrating the
                 effectiveness and scalability of our techniques.",
  acknowledgement = ack-nhfb,
}


bibtype: techreport
filename: pagerank.bib
label: Kamvar:2003:EBS
author: Sepandar D. Kamvar and Taher H. Haveliwala and Christopher D. Manning and Gene H. Golub
title: Exploiting the block structure of the Web for computing PageRank
type: Technical Report
number: 2003-17
institution: Stanford InfoLab, Stanford University
address: Stanford, CA, USA
year: 2003
bibdate: Fri Feb 19 15:17:26 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:17:26 ???
keywords: BlockRank; PageRank
acknowledgement: ack-nhfb
rawauthor: Sepandar D. Kamvar and Taher H. Haveliwala and Christopher D. Manning and Gene H. Golub
rawtitle: Exploiting the block structure of the {Web} for computing {PageRank}
entry:
@TechReport{Kamvar:2003:EBS,
  author =       "Sepandar D. Kamvar and Taher H. Haveliwala and
                 Christopher D. Manning and Gene H. Golub",
  title =        "Exploiting the block structure of the {Web} for
                 computing {PageRank}",
  type =         "Technical Report",
  number =       "2003-17",
  institution =  "Stanford InfoLab, Stanford University",
  address =      "Stanford, CA, USA",
  pages =        "????",
  year =         "2003",
  bibdate =      "Fri Feb 19 15:17:26 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "BlockRank; PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Kamvar:2003:EMA
author: Sepandar D. Kamvar and Taher H. Haveliwala and Christopher D. Manning and Gene H. Golub
title: Extrapolation Methods for Accelerating PageRank Computations
crossref: Hencsey:2003:PTI
pages: 261--270
year: 2003
DOI: https://doi.org/10.1145/775152.775190
bibdate: Wed Nov 10 16:22:54 2004
bibtimestamp: 2004.11.10 16:22:54 ???
keywords: eigenvector computation; link analysis; PageRank
acknowledgement: ack-nhfb
rawauthor: Sepandar D. Kamvar and Taher H. Haveliwala and Christopher D. Manning and Gene H. Golub
rawtitle: Extrapolation Methods for Accelerating {PageRank} Computations
bibsource:
https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
http://dbpubs.stanford.edu:8090/pub/2003-16; http://www.stanford.edu/~sdkamvar/papers/extrapolation.pdf

abstract:
We present a novel algorithm for the fast computation of PageRank, a hyperlink-based estimate of the ``importance'' of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Quadratic Extrapolation, accelerates the convergence of the Power Method by periodically subtracting off estimates of the nonprincipal eigenvectors from the current iterate of the Power Method. In Quadratic Extrapolation, we take advantage of the fact that the first eigenvalue of a Markov matrix is known to be 1 to compute the nonprincipal eigenvectors using successive iterates of the Power Method. Empirically, we show that using Quadratic Extrapolation speeds up PageRank computation by 25--300% on a Web graph of 80 million nodes, with minimal overhead. Our contribution is useful to the PageRank community and the numerical linear algebra community in general, as it is a fast method for determining the dominant eigenvector of a matrix that is too large for standard fast methods to be practical.

rawabstract:
We present a novel algorithm for the fast computation of PageRank, a hyperlink-based estimate of the ``importance'' of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Quadratic Extrapolation, accelerates the convergence of the Power Method by periodically subtracting off estimates of the nonprincipal eigenvectors from the current iterate of the Power Method. In Quadratic Extrapolation, we take advantage of the fact that the first eigenvalue of a Markov matrix is known to be 1 to compute the nonprincipal eigenvectors using successive iterates of the Power Method. Empirically, we show that using Quadratic Extrapolation speeds up PageRank computation by 25--300\% on a Web graph of 80 million nodes, with minimal overhead. Our contribution is useful to the PageRank community and the numerical linear algebra community in general, as it is a fast method for determining the dominant eigenvector of a matrix that is too large for standard fast methods to be practical.

entry:
@InProceedings{Kamvar:2003:EMA,
  author =       "Sepandar D. Kamvar and Taher H. Haveliwala and
                 Christopher D. Manning and Gene H. Golub",
  title =        "Extrapolation Methods for Accelerating {PageRank}
                 Computations",
  crossref =     "Hencsey:2003:PTI",
  pages =        "261--270",
  year =         "2003",
  DOI =          "https://doi.org/10.1145/775152.775190",
  bibdate =      "Wed Nov 10 16:22:54 2004",
  bibsource =    "https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://dbpubs.stanford.edu:8090/pub/2003-16;
                 http://www.stanford.edu/~sdkamvar/papers/extrapolation.pdf",
  abstract =     "We present a novel algorithm for the fast computation
                 of PageRank, a hyperlink-based estimate of the
                 ``importance'' of Web pages. The original PageRank
                 algorithm uses the Power Method to compute successive
                 iterates that converge to the principal eigenvector of
                 the Markov matrix representing the Web link graph. The
                 algorithm presented here, called Quadratic
                 Extrapolation, accelerates the convergence of the Power
                 Method by periodically subtracting off estimates of the
                 nonprincipal eigenvectors from the current iterate of
                 the Power Method. In Quadratic Extrapolation, we take
                 advantage of the fact that the first eigenvalue of a
                 Markov matrix is known to be 1 to compute the
                 nonprincipal eigenvectors using successive iterates of
                 the Power Method. Empirically, we show that using
                 Quadratic Extrapolation speeds up PageRank computation
                 by 25--300\% on a Web graph of 80 million nodes, with
                 minimal overhead. Our contribution is useful to the
                 PageRank community and the numerical linear algebra
                 community in general, as it is a fast method for
                 determining the dominant eigenvector of a matrix that
                 is too large for standard fast methods to be
                 practical.",
  acknowledgement = ack-nhfb,
  keywords =     "eigenvector computation; link analysis; PageRank",
}


bibtype: article
filename: pagerank.bib
label: Kang:2003:IPN
author: In-Ho Kang and Eun-Jung Oh and Gil Chang Kim
title: Incremental PageRanking for Newly Crawled Web Pages
journal: j-INT-J-COMP-PROC-ORIENTAL-LANG
volume: 16
number: 1
pages: 87--??
month: mar
monthnumber: 3
year: 2003
ISSN: 0219-4279 OR 02194279
bibdate: Thu Jan 06 07:59:01 2005
bibsource: http://ejournals.wspc.com.sg/ijcpol/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2005.01.06 07:59:01 ???
URL: https://www.math.utah.edu/pub/tex/bib/ijcpol.bib
fjournal: International Journal of Computer Processing of Oriental Languages (IJCPOL)
acknowledgement: ack-nhfb
rawauthor: In-Ho Kang and Eun-Jung Oh and Gil Chang Kim
rawtitle: Incremental {PageRanking} for Newly Crawled {Web} Pages
entry:
@Article{Kang:2003:IPN,
  author =       "In-Ho Kang and Eun-Jung Oh and Gil Chang Kim",
  title =        "Incremental {PageRanking} for Newly Crawled {Web}
                 Pages",
  journal =      j-INT-J-COMP-PROC-ORIENTAL-LANG,
  volume =       "16",
  number =       "1",
  pages =        "87--??",
  month =        mar,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4279",
  bibdate =      "Thu Jan 06 07:59:01 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijcpol/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://www.math.utah.edu/pub/tex/bib/ijcpol.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Computer Processing of
                 Oriental Languages (IJCPOL)",
}


bibtype: inproceedings
filename: pagerank.bib
label: Narayan:2003:TCW
author: B. L. Narayan and C. A. Murthy and S. K. Pal
title: Topic continuity for Web document categorization and ranking
crossref: Liu:2003:ISW
pages: 310--315
year: 2003
bibdate: Thu May 06 13:46:52 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 13:46:52 ???
acknowledgement: ack-nhfb
rawauthor: B. L. Narayan and C. A. Murthy and S. K. Pal
rawtitle: Topic continuity for {Web} document categorization and ranking
entry:
@InProceedings{Narayan:2003:TCW,
  author =       "B. L. Narayan and C. A. Murthy and S. K. Pal",
  title =        "Topic continuity for {Web} document categorization and
                 ranking",
  crossref =     "Liu:2003:ISW",
  pages =        "310--315",
  year =         "2003",
  bibdate =      "Thu May 06 13:46:52 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Ohmukai:2003:PPC
author: I. Ohmukai and H. Takeda and M. Miki
title: A proposal of the person-centered approach for personal task management
crossref: Helal:2003:SAI
pages: 234--240
year: 2003
bibdate: Thu May 06 13:51:30 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 13:51:30 ???
acknowledgement: ack-nhfb
rawauthor: I. Ohmukai and H. Takeda and M. Miki
rawtitle: A proposal of the person-centered approach for personal task management
abstract:
This paper proposes a human-centered approach for personal task management in which people can decide management of their tasks according to their environments, including their subjective and multivalent judgement and human relationships. In our approach task management is modeled as a decision-making process on their own resources. The human decision-making process consists of three types of activity, i.e., the intelligence activity, design activity, and choice activity. The proposed system assists each activity by three sub-systems, i.e., visualizer, optimizer and recommender respectively. At first, visualizer indicates the attributes associated with each task such as deadline, subjective priority, and workload, which are determined by the user. The optimizer generates executable schedules from these tasks using an active scheduler and multi-objective genetic algorithm. Finally, the recommender evaluates these alternatives using an analytic hierarchy process. The system is also able to analyze the human relationships of the user group using the PageRank algorithm, and this result is utilized to improve the performance of the task scheduler. We implement a client/server system which uses mobile phones and verify the function of the proposed system along the lines of two scenarios.

rawabstract:
This paper proposes a human-centered approach for personal task management in which people can decide management of their tasks according to their environments, including their subjective and multivalent judgement and human relationships. In our approach task management is modeled as a decision-making process on their own resources. The human decision-making process consists of three types of activity, i.e., the intelligence activity, design activity, and choice activity. The proposed system assists each activity by three sub-systems, i.e., visualizer, optimizer and recommender respectively. At first, visualizer indicates the attributes associated with each task such as deadline, subjective priority, and workload, which are determined by the user. The optimizer generates executable schedules from these tasks using an active scheduler and multi-objective genetic algorithm. Finally, the recommender evaluates these alternatives using an analytic hierarchy process. The system is also able to analyze the human relationships of the user group using the PageRank algorithm, and this result is utilized to improve the performance of the task scheduler. We implement a client/server system which uses mobile phones and verify the function of the proposed system along the lines of two scenarios.

entry:
@InProceedings{Ohmukai:2003:PPC,
  author =       "I. Ohmukai and H. Takeda and M. Miki",
  title =        "A proposal of the person-centered approach for
                 personal task management",
  crossref =     "Helal:2003:SAI",
  pages =        "234--240",
  year =         "2003",
  bibdate =      "Thu May 06 13:51:30 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This paper proposes a human-centered approach for
                 personal task management in which people can decide
                 management of their tasks according to their
                 environments, including their subjective and
                 multivalent judgement and human relationships. In our
                 approach task management is modeled as a
                 decision-making process on their own resources. The
                 human decision-making process consists of three types
                 of activity, i.e., the intelligence activity, design
                 activity, and choice activity. The proposed system
                 assists each activity by three sub-systems, i.e.,
                 visualizer, optimizer and recommender respectively. At
                 first, visualizer indicates the attributes associated
                 with each task such as deadline, subjective priority,
                 and workload, which are determined by the user. The
                 optimizer generates executable schedules from these
                 tasks using an active scheduler and multi-objective
                 genetic algorithm. Finally, the recommender evaluates
                 these alternatives using an analytic hierarchy process.
                 The system is also able to analyze the human
                 relationships of the user group using the PageRank
                 algorithm, and this result is utilized to improve the
                 performance of the task scheduler. We implement a
                 client/server system which uses mobile phones and
                 verify the function of the proposed system along the
                 lines of two scenarios.",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Sankaralingam:2003:DPP
author: Karthikeyan Sankaralingam and Simha Sethumadhavan and James C. Browne
title: Distributed PageRank for P2P systems
crossref: IEEE:2003:IIS
pages: 58--68
year: 2003
DOI: https://doi.org/10.1109/HPDC.2003.1210016
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1210016
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8591
rawauthor: Karthikeyan Sankaralingam and Simha Sethumadhavan and James C. Browne
rawtitle: Distributed {PageRank} for {P2P} systems
abstract:
This paper defines and describes a fully distributed implementation of Google's highly effective PageRank algorithm, for 'peer to peer' (P2P) systems. The implementation is based on chaotic (asynchronous) iterative solution of linear systems. The P2P implementation also enables incremental computation of pageranks as new documents are entered into or deleted from the network. Incremental update enables continuously accurate pageranks whereas the currently centralized web crawl and computation over Internet documents requires several days. This suggests possible applicability of the distributed algorithm to pagerank computations as a replacement for the centralized web crawler based implementation for Internet documents. A complete solution of the distributed pagerank computation for an inplace network converges rapidly (1% accuracy in 10 iterations) for large systems although the time for an iteration may be long. The incremental computation resulting from addition of a single document converges extremely rapidly, typically requiring update path lengths of under 15 nodes even for large networks and very accurate solutions. This implementation of PageRank provides a uniform ranking scheme for documents in P2P systems, and its integration with P2P keyword search provides one solution to the network traffic problems engendered by return of document hits. In basic P2P keyword search, all the document hits must be returned to the querying node causing large network traffic. An incremental keyword search algorithm for P2P keyword search where document hits are sorted by pagerank, and incrementally returned to the querying node is proposed and evaluated. Integration of this algorithm into P2P keyword search can produce dramatic benefit both in terms of effectiveness for users and decrease in network traffic. The incremental search algorithm provided approximately a ten-fold reduction in network traffic for two-word and three-word queries.

rawabstract:
This paper defines and describes a fully distributed implementation of Google's highly effective PageRank algorithm, for 'peer to peer' (P2P) systems. The implementation is based on chaotic (asynchronous) iterative solution of linear systems. The P2P implementation also enables incremental computation of pageranks as new documents are entered into or deleted from the network. Incremental update enables continuously accurate pageranks whereas the currently centralized web crawl and computation over Internet documents requires several days. This suggests possible applicability of the distributed algorithm to pagerank computations as a replacement for the centralized web crawler based implementation for Internet documents. A complete solution of the distributed pagerank computation for an inplace network converges rapidly (1\% accuracy in 10 iterations) for large systems although the time for an iteration may be long. The incremental computation resulting from addition of a single document converges extremely rapidly, typically requiring update path lengths of under 15 nodes even for large networks and very accurate solutions. This implementation of PageRank provides a uniform ranking scheme for documents in P2P systems, and its integration with P2P keyword search provides one solution to the network traffic problems engendered by return of document hits. In basic P2P keyword search, all the document hits must be returned to the querying node causing large network traffic. An incremental keyword search algorithm for P2P keyword search where document hits are sorted by pagerank, and incrementally returned to the querying node is proposed and evaluated. Integration of this algorithm into P2P keyword search can produce dramatic benefit both in terms of effectiveness for users and decrease in network traffic. The incremental search algorithm provided approximately a ten-fold reduction in network traffic for two-word and three-word queries.

entry:
@InProceedings{Sankaralingam:2003:DPP,
  author =       "Karthikeyan Sankaralingam and Simha Sethumadhavan and
                 James C. Browne",
  title =        "Distributed {PageRank} for {P2P} systems",
  crossref =     "IEEE:2003:IIS",
  pages =        "58--68",
  year =         "2003",
  DOI =          "https://doi.org/10.1109/HPDC.2003.1210016",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1210016",
  abstract =     "This paper defines and describes a fully distributed
                 implementation of Google's highly effective PageRank
                 algorithm, for 'peer to peer' (P2P) systems. The
                 implementation is based on chaotic (asynchronous)
                 iterative solution of linear systems. The P2P
                 implementation also enables incremental computation of
                 pageranks as new documents are entered into or deleted
                 from the network. Incremental update enables
                 continuously accurate pageranks whereas the currently
                 centralized web crawl and computation over Internet
                 documents requires several days. This suggests possible
                 applicability of the distributed algorithm to pagerank
                 computations as a replacement for the centralized web
                 crawler based implementation for Internet documents. A
                 complete solution of the distributed pagerank
                 computation for an inplace network converges rapidly
                 (1\% accuracy in 10 iterations) for large systems
                 although the time for an iteration may be long. The
                 incremental computation resulting from addition of a
                 single document converges extremely rapidly, typically
                 requiring update path lengths of under 15 nodes even
                 for large networks and very accurate solutions. This
                 implementation of PageRank provides a uniform ranking
                 scheme for documents in P2P systems, and its
                 integration with P2P keyword search provides one
                 solution to the network traffic problems engendered by
                 return of document hits. In basic P2P keyword search,
                 all the document hits must be returned to the querying
                 node causing large network traffic. An incremental
                 keyword search algorithm for P2P keyword search where
                 document hits are sorted by pagerank, and incrementally
                 returned to the querying node is proposed and
                 evaluated. Integration of this algorithm into P2P
                 keyword search can produce dramatic benefit both in
                 terms of effectiveness for users and decrease in
                 network traffic. The incremental search algorithm
                 provided approximately a ten-fold reduction in network
                 traffic for two-word and three-word queries.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8591",
}


bibtype: article
filename: pagerank.bib
label: Sankaralingam:2003:PCK
author: Karthikeyan Sankaralingam and Madhulika Yalamanchi and Simha Sethumadhavan and James C. Browne
title: Pagerank Computation and Keyword Search on Distributed Systems and P2P Networks
journal: j-J-GRID-COMP
volume: 1
number: 3
pages: 291--307
year: 2003
ISSN: 1570-7873 (print), 1572-9184 (electronic) OR 15707873 (print), 15729184 (electronic)
ISSNL: 1570-7873 OR 15707873
bibdate: Sat Dec 4 11:39:32 MST 2004
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.wkap.nl/jrnltoc.htm/1570-7873
bibtimestamp: 2004.12.04 11:39:32 MST
fjournal: Journal of Grid Computing
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/10723
rawauthor: Karthikeyan Sankaralingam and Madhulika Yalamanchi and Simha Sethumadhavan and James C. Browne
rawtitle: {Pagerank} Computation and Keyword Search on Distributed Systems and {P2P} Networks
URL:
http://ipsapp008.kluweronline.com/IPS/content/ext/x/J/6160/I/9/A/6/abstract.htm; https://www.math.utah.edu/pub/tex/bib/jgridcomp.bib

abstract:
This paper presents a fully distributed computation for Google's pagerank algorithm. The computation is based on solution of the matrix equation defining pageranks by a distributed implementation of asynchronous iteration. Pageranks for the documents stored on a web server or on a host in a peer-to-peer network are computed in place and stored with the documents. The matrix is never assembled and no crawls of the web are required. Continuously accurate pageranks are enabled by incremental computation of pageranks for documents as they are inserted onto a network storage host and incremental recomputation of pageranks when documents are deleted. Intrahost and intradomain dominance of document link structure is naturally exploited by the distributed asynchronous iteration algorithm.par Three implementations: (i) a simulation which was previously reported, (ii) an implementation of the algorithm in a peer-to-peer computational system and (iii) an embedding of the computation in web servers, are described. Application of the three implementations to three different workloads, two constructed following power law network models for link distributions and one derived from the Government document database are reported. Convergence for computation of a complete set of pageranks is rapid: 1% accuracy in 10 or fewer messages per document. Incremental computation of pageranks resulting from addition or deletion of documents also converges rapidly, usually requiring 10 or fewer messages per document. Coupling locally stored pageranks with the documents in a peer-to-peer network dramatically diminishes the volume of data which must be transmitted to satisfy keyword searches in peer-to-peer networks.par The web server implementation shows that the distributed algorithm can be used to enable web servers to compute pageranks for the documents they store and thus potentially enable effective keyword searches for the documents stored on the web servers of intranets by utilizing unused processing power of the web servers.

rawabstract:
This paper presents a fully distributed computation for Google's pagerank algorithm. The computation is based on solution of the matrix equation defining pageranks by a distributed implementation of asynchronous iteration. Pageranks for the documents stored on a web server or on a host in a peer-to-peer network are computed in place and stored with the documents. The matrix is never assembled and no crawls of the web are required. Continuously accurate pageranks are enabled by incremental computation of pageranks for documents as they are inserted onto a network storage host and incremental recomputation of pageranks when documents are deleted. Intrahost and intradomain dominance of document link structure is naturally exploited by the distributed asynchronous iteration algorithm.\par Three implementations: (i) a simulation which was previously reported, (ii) an implementation of the algorithm in a peer-to-peer computational system and (iii) an embedding of the computation in web servers, are described. Application of the three implementations to three different workloads, two constructed following power law network models for link distributions and one derived from the Government document database are reported. Convergence for computation of a complete set of pageranks is rapid: 1\% accuracy in 10 or fewer messages per document. Incremental computation of pageranks resulting from addition or deletion of documents also converges rapidly, usually requiring 10 or fewer messages per document. Coupling locally stored pageranks with the documents in a peer-to-peer network dramatically diminishes the volume of data which must be transmitted to satisfy keyword searches in peer-to-peer networks.\par The web server implementation shows that the distributed algorithm can be used to enable web servers to compute pageranks for the documents they store and thus potentially enable effective keyword searches for the documents stored on the web servers of intranets by utilizing unused processing power of the web servers.

entry:
@Article{Sankaralingam:2003:PCK,
  author =       "Karthikeyan Sankaralingam and Madhulika Yalamanchi and
                 Simha Sethumadhavan and James C. Browne",
  title =        "{Pagerank} Computation and Keyword Search on
                 Distributed Systems and {P2P} Networks",
  journal =      j-J-GRID-COMP,
  volume =       "1",
  number =       "3",
  pages =        "291--307",
  month =        "????",
  year =         "2003",
  CODEN =        "????",
  ISSN =         "1570-7873 (print), 1572-9184 (electronic)",
  ISSN-L =       "1570-7873",
  bibdate =      "Sat Dec 4 11:39:32 MST 2004",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.wkap.nl/jrnltoc.htm/1570-7873",
  URL =          "http://ipsapp008.kluweronline.com/IPS/content/ext/x/J/6160/I/9/A/6/abstract.htm;
                 https://www.math.utah.edu/pub/tex/bib/jgridcomp.bib",
  abstract =     "This paper presents a fully distributed computation
                 for Google's pagerank algorithm. The computation is
                 based on solution of the matrix equation defining
                 pageranks by a distributed implementation of
                 asynchronous iteration. Pageranks for the documents
                 stored on a web server or on a host in a peer-to-peer
                 network are computed in place and stored with the
                 documents. The matrix is never assembled and no crawls
                 of the web are required. Continuously accurate
                 pageranks are enabled by incremental computation of
                 pageranks for documents as they are inserted onto a
                 network storage host and incremental recomputation of
                 pageranks when documents are deleted. Intrahost and
                 intradomain dominance of document link structure is
                 naturally exploited by the distributed asynchronous
                 iteration algorithm.\par

                 Three implementations: (i) a simulation which was
                 previously reported, (ii) an implementation of the
                 algorithm in a peer-to-peer computational system and
                 (iii) an embedding of the computation in web servers,
                 are described. Application of the three implementations
                 to three different workloads, two constructed following
                 power law network models for link distributions and one
                 derived from the Government document database are
                 reported. Convergence for computation of a complete set
                 of pageranks is rapid: 1\% accuracy in 10 or fewer
                 messages per document. Incremental computation of
                 pageranks resulting from addition or deletion of
                 documents also converges rapidly, usually requiring 10
                 or fewer messages per document. Coupling locally stored
                 pageranks with the documents in a peer-to-peer network
                 dramatically diminishes the volume of data which must
                 be transmitted to satisfy keyword searches in
                 peer-to-peer networks.\par

                 The web server implementation shows that the
                 distributed algorithm can be used to enable web servers
                 to compute pageranks for the documents they store and
                 thus potentially enable effective keyword searches for
                 the documents stored on the web servers of intranets by
                 utilizing unused processing power of the web servers.",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Grid Computing",
  journal-URL =  "http://link.springer.com/journal/10723",
}


bibtype: inproceedings
filename: pagerank.bib
label: Shi:2003:DPR
author: ShuMing Shi and Jin Yu and GuangWen Yang and DingXing Wang
title: Distributed page ranking in structured P2P networks
crossref: Yang:2003:ICP
pages: 179--186
year: 2003
DOI: https://doi.org/10.1109/ICPP.2003.1240579
bibdate: Thu May 06 13:52:53 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 13:52:53 ???
acknowledgement: ack-nhfb
rawauthor: ShuMing Shi and Jin Yu and GuangWen Yang and DingXing Wang
rawtitle: Distributed page ranking in structured {P2P} networks
abstract:
We discuss the techniques of performing distributed page ranking on top of structured peer-to-peer networks. Distributed page ranking are needed because the size of the Web grows at a remarkable speed and centralized page ranking is not scalable. Open system PageRank is presented based on the traditional PageRank used by Google. We then propose some distributed page ranking algorithms, partially prove their convergence, and discuss some interesting properties of them. Indirect transmission is introduced to reduce communication overhead between page rankers and to achieve scalable communication. The relationship between convergence time and bandwidth consumed is also discussed. Finally, we verify some of the discussions by experiments based on real datasets.

rawabstract:
We discuss the techniques of performing distributed page ranking on top of structured peer-to-peer networks. Distributed page ranking are needed because the size of the Web grows at a remarkable speed and centralized page ranking is not scalable. Open system PageRank is presented based on the traditional PageRank used by Google. We then propose some distributed page ranking algorithms, partially prove their convergence, and discuss some interesting properties of them. Indirect transmission is introduced to reduce communication overhead between page rankers and to achieve scalable communication. The relationship between convergence time and bandwidth consumed is also discussed. Finally, we verify some of the discussions by experiments based on real datasets.

entry:
@InProceedings{Shi:2003:DPR,
  author =       "ShuMing Shi and Jin Yu and GuangWen Yang and DingXing
                 Wang",
  title =        "Distributed page ranking in structured {P2P}
                 networks",
  crossref =     "Yang:2003:ICP",
  pages =        "179--186",
  year =         "2003",
  DOI =          "https://doi.org/10.1109/ICPP.2003.1240579",
  bibdate =      "Thu May 06 13:52:53 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We discuss the techniques of performing distributed
                 page ranking on top of structured peer-to-peer
                 networks. Distributed page ranking are needed because
                 the size of the Web grows at a remarkable speed and
                 centralized page ranking is not scalable. Open system
                 PageRank is presented based on the traditional PageRank
                 used by Google. We then propose some distributed page
                 ranking algorithms, partially prove their convergence,
                 and discuss some interesting properties of them.
                 Indirect transmission is introduced to reduce
                 communication overhead between page rankers and to
                 achieve scalable communication. The relationship
                 between convergence time and bandwidth consumed is also
                 discussed. Finally, we verify some of the discussions
                 by experiments based on real datasets.",
  acknowledgement = ack-nhfb,
}


bibtype: misc
filename: pagerank.bib
label: Sobek:2003:PGP
author: M. Sobek
title: PR0 --- Google's PageRank $0$ Penalty
pages: 179--186
year: 2003
bibdate: Tue Aug 11 17:36:01 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:36:01 ???
URL: http://pr.efactory.de/e-pr0.shtml
acknowledgement: ack-nhfb
howpublished: Web document.
rawauthor: M. Sobek
rawtitle: {PR0} --- {Google}'s {PageRank} $0$ Penalty
abstract:
By the end of 2001, the Google search engine introduced a new kind of penalty for websites that use questionable search engine optimization tactics: A PageRank of 0. In search engine optimization forums it is called PR0 and this term shall also be used here. Characteristically for PR0 is that all or at least a lot of pages of a website show a PageRank of 0 in the Google Toolbar, even if they do have high quality inbound links. Those pages are not completely removed from the index but they are always at the end of search results and, thus, they are hardly to be found.

rawabstract:
By the end of 2001, the Google search engine introduced a new kind of penalty for websites that use questionable search engine optimization tactics: A PageRank of 0. In search engine optimization forums it is called PR0 and this term shall also be used here. Characteristically for PR0 is that all or at least a lot of pages of a website show a PageRank of 0 in the Google Toolbar, even if they do have high quality inbound links. Those pages are not completely removed from the index but they are always at the end of search results and, thus, they are hardly to be found.

entry:
@Misc{Sobek:2003:PGP,
  author =       "M. Sobek",
  title =        "{PR0} --- {Google}'s {PageRank} $0$ Penalty",
  howpublished = "Web document.",
  year =         "2003",
  bibdate =      "Tue Aug 11 17:36:01 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://pr.efactory.de/e-pr0.shtml",
  abstract =     "By the end of 2001, the Google search engine
                 introduced a new kind of penalty for websites that use
                 questionable search engine optimization tactics: A
                 PageRank of 0. In search engine optimization forums it
                 is called PR0 and this term shall also be used here.
                 Characteristically for PR0 is that all or at least a
                 lot of pages of a website show a PageRank of 0 in the
                 Google Toolbar, even if they do have high quality
                 inbound links. Those pages are not completely removed
                 from the index but they are always at the end of search
                 results and, thus, they are hardly to be found.",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Tao:2003:QSS
author: Wen-Xue Tao and Wan-Li Zuo
booktitle: International Conference on Machine Learning and Cybernetics, 2003
title: Query-sensitive self-adaptable Web page ranking algorithm
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 413--418
year: 2003
ISBN: 0-7803-8131-9 OR 0780381319
ISBN13: 978-0-7803-8131-5 OR 9780780381315
bibdate: Thu May 06 13:40:45 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 13:40:45 ???
acknowledgement: ack-nhfb
rawauthor: Wen-Xue Tao and Wan-Li Zuo
rawbooktitle: {International Conference on Machine Learning and Cybernetics, 2003}
rawtitle: Query-sensitive self-adaptable {Web} page ranking algorithm
abstract:
This paper analyzes HITS and PageRank, two representative examples of current Web page ranking algorithms, and points out their limitations in capturing both global and local importance scopes. A detailed discussion is also conducted regarding the reasons why manually setting topics adopted by topic-sensitive PageRank algorithm cannot resolve the same problem. Based on the above observation, a new query-sensitive algorithm termed QS page-rank satisfying both global and local authority is introduced, and several strategies for combining our algorithm with traditional PageRank are also proposed. Experiment results show effectiveness of the new page ranking algorithm.

rawabstract:
This paper analyzes HITS and PageRank, two representative examples of current Web page ranking algorithms, and points out their limitations in capturing both global and local importance scopes. A detailed discussion is also conducted regarding the reasons why manually setting topics adopted by topic-sensitive PageRank algorithm cannot resolve the same problem. Based on the above observation, a new query-sensitive algorithm termed QS page-rank satisfying both global and local authority is introduced, and several strategies for combining our algorithm with traditional PageRank are also proposed. Experiment results show effectiveness of the new page ranking algorithm.

entry:
@InProceedings{Tao:2003:QSS,
  author =       "Wen-Xue Tao and Wan-Li Zuo",
  booktitle =    "{International Conference on Machine Learning and
                 Cybernetics, 2003}",
  title =        "Query-sensitive self-adaptable {Web} page ranking
                 algorithm",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "413--418",
  year =         "2003",
  ISBN =         "0-7803-8131-9",
  ISBN-13 =      "978-0-7803-8131-5",
  LCCN =         "????",
  bibdate =      "Thu May 06 13:40:45 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This paper analyzes HITS and PageRank, two
                 representative examples of current Web page ranking
                 algorithms, and points out their limitations in
                 capturing both global and local importance scopes. A
                 detailed discussion is also conducted regarding the
                 reasons why manually setting topics adopted by
                 topic-sensitive PageRank algorithm cannot resolve the
                 same problem. Based on the above observation, a new
                 query-sensitive algorithm termed QS page-rank
                 satisfying both global and local authority is
                 introduced, and several strategies for combining our
                 algorithm with traditional PageRank are also proposed.
                 Experiment results show effectiveness of the new page
                 ranking algorithm.",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Tomlin:2003:NPR
author: John A. Tomlin
editor: Bebo White and Gusztav Hencsey
booktitle: Proceedings of the 12th International Conference on the World Wide Web, WWW '03
title: A new paradigm for ranking pages on the World Wide Web, Budapest, Hungary, May 20--24, 2003
publisher: pub-ACM
address: pub-ACM:adr
pages: 350--355
year: 2003
DOI: https://doi.org/10.1145/775152.775202
ISBN: 1-58113-680-3 OR 1581136803
ISBN13: 978-1-58113-680-7 OR 9781581136807
LCCN: TK5105.888 I573 2003
bibdate: Tue Aug 11 17:37:30 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:37:30 ???
acknowledgement: ack-nhfb
bookpages: xx + 752
rawauthor: John A. Tomlin
rawbooktitle: {Proceedings of the 12th International Conference on the World Wide Web, WWW '03}
raweditor: Bebo White and Gusztav Hencsey
rawtitle: A new paradigm for ranking pages on the {World Wide Web, Budapest, Hungary, May 20--24, 2003}
entry:
@InProceedings{Tomlin:2003:NPR,
  author =       "John A. Tomlin",
  editor =       "Bebo White and Gusztav Hencsey",
  booktitle =    "{Proceedings of the 12th International Conference on
                 the World Wide Web, WWW '03}",
  title =        "A new paradigm for ranking pages on the {World Wide
                 Web, Budapest, Hungary, May 20--24, 2003}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "350--355",
  year =         "2003",
  DOI =          "https://doi.org/10.1145/775152.775202",
  ISBN =         "1-58113-680-3",
  ISBN-13 =      "978-1-58113-680-7",
  LCCN =         "TK5105.888 I573 2003",
  bibdate =      "Tue Aug 11 17:37:30 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "xx + 752",
}


bibtype: inproceedings
filename: pagerank.bib
label: Acharyya:2004:OEP
author: Sreangsu Acharyya and Joydeep Ghosh
editor: ACM
title: Outlink estimation for PageRank computation under missing data
publisher: pub-ACM
address: pub-ACM:adr
pages: 486--487
year: 2004
ISBN: 1-58113-912-8 OR 1581139128
ISBN13: 978-1-58113-912-9 OR 9781581139129
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
acknowledgement: ack-nhfb
rawauthor: Sreangsu Acharyya and Joydeep Ghosh
raweditor: {ACM}
rawtitle: Outlink estimation for {PageRank} computation under missing data
booktitle:
International World Wide Web Conference Proceedings of the 13th international World Wide Web conference: Alternate track papers & posters

abstract:
The enormity and rapid growth of the web-graph forces quantities such as its pagerank to be computed under missing information consisting of outlinks of pages that have not yet been crawled. This paper examines the role played by the size and distribution of this missing data in determining the accuracy of the computed pagerank, focusing on questions such as (i) the accuracy of pageranks under missing information, (ii) the size at which a crawl process may be aborted while still ensuring reasonable accuracy of pageranks, and (iii) algorithms to estimate pageranks under such missing information. The first couple of questions are addressed on the basis of certain simple bounds relating the expected distance between the true and computed pageranks and the size of the missing data. The third question is explored by devising algorithms to predict the pageranks when full information is not available. A key feature of the 'dangling link estimation' and 'clustered link estimation' algorithms proposed is that, they do not need to run the pagerank iteration afresh once the outlinks have been estimated.

rawabstract:
The enormity and rapid growth of the web-graph forces quantities such as its pagerank to be computed under missing information consisting of outlinks of pages that have not yet been crawled. This paper examines the role played by the size and distribution of this missing data in determining the accuracy of the computed pagerank, focusing on questions such as (i) the accuracy of pageranks under missing information, (ii) the size at which a crawl process may be aborted while still ensuring reasonable accuracy of pageranks, and (iii) algorithms to estimate pageranks under such missing information. The first couple of questions are addressed on the basis of certain simple bounds relating the expected distance between the true and computed pageranks and the size of the missing data. The third question is explored by devising algorithms to predict the pageranks when full information is not available. A key feature of the 'dangling link estimation' and 'clustered link estimation' algorithms proposed is that, they do not need to run the pagerank iteration afresh once the outlinks have been estimated.

rawbooktitle:
{International World Wide Web Conference Proceedings of the 13th international World Wide Web conference: Alternate track papers \& posters}

entry:
@InProceedings{Acharyya:2004:OEP,
  author =       "Sreangsu Acharyya and Joydeep Ghosh",
  editor =       "{ACM}",
  booktitle =    "{International World Wide Web Conference Proceedings
                 of the 13th international World Wide Web conference:
                 Alternate track papers \& posters}",
  title =        "Outlink estimation for {PageRank} computation under
                 missing data",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "486--487",
  year =         "2004",
  ISBN =         "1-58113-912-8",
  ISBN-13 =      "978-1-58113-912-9",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:05 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The enormity and rapid growth of the web-graph forces
                 quantities such as its pagerank to be computed under
                 missing information consisting of outlinks of pages
                 that have not yet been crawled. This paper examines the
                 role played by the size and distribution of this
                 missing data in determining the accuracy of the
                 computed pagerank, focusing on questions such as (i)
                 the accuracy of pageranks under missing information,
                 (ii) the size at which a crawl process may be aborted
                 while still ensuring reasonable accuracy of pageranks,
                 and (iii) algorithms to estimate pageranks under such
                 missing information. The first couple of questions are
                 addressed on the basis of certain simple bounds
                 relating the expected distance between the true and
                 computed pageranks and the size of the missing data.
                 The third question is explored by devising algorithms
                 to predict the pageranks when full information is not
                 available. A key feature of the 'dangling link
                 estimation' and 'clustered link estimation' algorithms
                 proposed is that, they do not need to run the pagerank
                 iteration afresh once the outlinks have been
                 estimated.",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Altman:2004:RSP
author: Alon Altman
title: Ranking systems: the PageRank axioms
volume: 05011
publisher: Internat. Begegnungs- und Forschungszentrum fur Informatik
pages: 486--487
year: 2004
bibdate: Fri Feb 19 15:35:56 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:35:56 ???
series: Dagstuhl seminar proceedings
acknowledgement: ack-nhfb
rawauthor: Alon Altman
rawtitle: Ranking systems: the {PageRank} axioms
entry:
@InProceedings{Altman:2004:RSP,
  author =       "Alon Altman",
  booktitle =    "????",
  title =        "Ranking systems: the {PageRank} axioms",
  volume =       "05011",
  publisher =    "Internat. Begegnungs- und Forschungszentrum f{\"u}r
                 Informatik",
  year =         "2004",
  bibdate =      "Fri Feb 19 15:35:56 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  acknowledgement = ack-nhfb,
}


bibtype: misc
filename: pagerank.bib
label: Anonymous:2004:BGB
author: Anonymous
title: Biography: The Google boys
address: United States
pages: 486--487
day: 18
month: dec
monthnumber: 12
year: 2004
bibdate: Fri Jun 3 09:47:20 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager
bibtimestamp: 2011.06.03 09:47:20 MDT
note: 1 50-minute VHS videocassette.
subject: Brin, Sergey; Page, Larry; Internet industry; United States; History; Businesspeople; Biography
acknowledgement: ack-nhfb
howpublished: A&E Television Networks
subjectdates: 1973--; 1973--
rawauthor: Anonymous
rawnote: 1 50-minute VHS videocassette.
rawtitle: Biography: The {Google} boys
abstract:
Profiles Sergey Brin and Larry Page, two Stanford University computer science Ph.D candidates who went on to develop the world's most popular search engine.

rawabstract:
Profiles Sergey Brin and Larry Page, two Stanford University computer science Ph.D candidates who went on to develop the world's most popular search engine.

entry:
@Misc{Anonymous:2004:BGB,
  author =       "Anonymous",
  title =        "Biography: The {Google} boys",
  howpublished = "A\&E Television Networks",
  address =      "United States",
  day =          "18",
  month =        dec,
  year =         "2004",
  bibdate =      "Fri Jun 3 09:47:20 MDT 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  note =         "1 50-minute VHS videocassette.",
  abstract =     "Profiles Sergey Brin and Larry Page, two Stanford
                 University computer science Ph.D candidates who went on
                 to develop the world's most popular search engine.",
  acknowledgement = ack-nhfb,
  subject =      "Brin, Sergey; Page, Larry; Internet industry; United
                 States; History; Businesspeople; Biography",
  subject-dates = "1973--; 1973--",
}


bibtype: inproceedings
filename: pagerank.bib
label: Balmin:2004:OAB
author: A. Balmin and V. Hristidis and Y. Papakonstantinou
title: ObjectRank: Authority-based keyword search in databases
publisher: pub-MORGAN-KAUFMANN
address: pub-MORGAN-KAUFMANN:adr
pages: 564--575
year: 2004
ISBN: 0-12-088469-0 (paperback) OR 0120884690 (paperback)
ISBN13: 978-0-12-088469-8 (paperback) OR 9780120884698 (paperback)
LCCN: QA76.9.D3 I559 2004
bibdate: Tue Aug 11 15:55:54 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 15:55:54 ???
acknowledgement: ack-nhfb
bookpages: 1380
rawauthor: A. Balmin and V. Hristidis and Y. Papakonstantinou
rawtitle: {ObjectRank}: Authority-based keyword search in databases
editor:
Mario A. Nascimento and M. Tamer Ozsu and Donald Kossmann and Renee J. Miller and Jose A. Blakeley and K. Bernhard Schiefer

booktitle:
Proceedings of the Thirtieth International Conference on Very Large Data Bases: VLDB '04. Toronto, Canada, Aug. 31--Sept. 3, 2004

rawbooktitle:
Proceedings of the Thirtieth International Conference on Very Large Data Bases: VLDB '04. Toronto, Canada, Aug. 31--Sept. 3, 2004

raweditor:
Mario A. Nascimento and M. Tamer {\"O}zsu and Donald Kossmann and Ren{\'e}e J. Miller and Jos{\'e} A. Blakeley and K. Bernhard Schiefer

entry:
@InProceedings{Balmin:2004:OAB,
  author =       "A. Balmin and V. Hristidis and Y. Papakonstantinou",
  editor =       "Mario A. Nascimento and M. Tamer {\"O}zsu and Donald
                 Kossmann and Ren{\'e}e J. Miller and Jos{\'e} A.
                 Blakeley and K. Bernhard Schiefer",
  booktitle =    "Proceedings of the Thirtieth International Conference
                 on Very Large Data Bases: VLDB '04. Toronto, Canada,
                 Aug. 31--Sept. 3, 2004",
  title =        "{ObjectRank}: Authority-based keyword search in
                 databases",
  publisher =    pub-MORGAN-KAUFMANN,
  address =      pub-MORGAN-KAUFMANN:adr,
  pages =        "564--575",
  year =         "2004",
  ISBN =         "0-12-088469-0 (paperback)",
  ISBN-13 =      "978-0-12-088469-8 (paperback)",
  LCCN =         "QA76.9.D3 I559 2004",
  bibdate =      "Tue Aug 11 15:55:54 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "1380",
}


bibtype: article
filename: pagerank.bib
label: Blondel:2004:MSB
author: Vincent D. Blondel and Anahi Gajardo and Maureen Heymans and Pierre Senellart and Paul Van Dooren
journal: j-SIAM-REVIEW
volume: 46
number: 4
pages: 647--666
month: dec
monthnumber: 12
year: 2004
CODEN: SIREAD
DOI: https://doi.org/10.1137/S0036144502415960
ISSN: 0036-1445 (print), 1095-7200 (electronic) OR 00361445 (print), 10957200 (electronic)
ISSNL: 0036-1445 OR 00361445
bibdate: Sat Mar 29 09:56:54 MDT 2014
bibtimestamp: 2014.03.29 09:56:54 MDT
URL: http://epubs.siam.org/sam-bin/dbq/article/41596
fjournal: SIAM Review
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sirev
onlinedate: January 2004
title:
A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching

bibsource:
http://epubs.siam.org/toc/siread/46/4; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/siamreview.bib

rawauthor:
Vincent D. Blondel and Anah{\'\i} Gajardo and Maureen Heymans and Pierre Senellart and Paul {Van Dooren}

rawtitle:
A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and {Web} Searching

entry:
@Article{Blondel:2004:MSB,
  author =       "Vincent D. Blondel and Anah{\'\i} Gajardo and Maureen
                 Heymans and Pierre Senellart and Paul {Van Dooren}",
  title =        "A Measure of Similarity between Graph Vertices:
                 Applications to Synonym Extraction and {Web}
                 Searching",
  journal =      j-SIAM-REVIEW,
  volume =       "46",
  number =       "4",
  pages =        "647--666",
  month =        dec,
  year =         "2004",
  CODEN =        "SIREAD",
  DOI =          "https://doi.org/10.1137/S0036144502415960",
  ISSN =         "0036-1445 (print), 1095-7200 (electronic)",
  ISSN-L =       "0036-1445",
  bibdate =      "Sat Mar 29 09:56:54 MDT 2014",
  bibsource =    "http://epubs.siam.org/toc/siread/46/4;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/siamreview.bib",
  URL =          "http://epubs.siam.org/sam-bin/dbq/article/41596",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Review",
  journal-URL =  "http://epubs.siam.org/sirev",
  onlinedate =   "January 2004",
}


bibtype: inproceedings
filename: pagerank.bib
label: Boldi:2004:DYW
author: Paolo Boldi and Massimo Santini and Sebastiano Vigna
title: Do your worst to make the best: Paradoxical effects in PageRank incremental computations
crossref: Leonardi:2004:AMW
pages: 168--180
year: 2004
MRclass: 68M10
bibdate: Thu May 06 12:24:30 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 12:24:30 ???
ZMnumber: 1109.68325
acknowledgement: ack-nhfb
rawauthor: Paolo Boldi and Massimo Santini and Sebastiano Vigna
rawtitle: Do your worst to make the best: Paradoxical effects in {PageRank} incremental computations
entry:
@InProceedings{Boldi:2004:DYW,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
  title =        "Do your worst to make the best: Paradoxical effects in
                 {PageRank} incremental computations",
  crossref =     "Leonardi:2004:AMW",
  pages =        "168--180",
  year =         "2004",
  MRclass =      "68M10",
  bibdate =      "Thu May 06 12:24:30 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1109.68325",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Broder:2004:EPA
author: Andrei Z. Broder and Ronny Lempel and Farzin Maghoul and Jan Pedersen
editor: ACM
title: Efficient PageRank approximation via graph aggregation
publisher: pub-ACM
address: pub-ACM:adr
pages: 484--485
year: 2004
DOI: https://doi.org/10.1145/1013367.1013537
ISBN: 1-58113-912-8 OR 1581139128
ISBN13: 978-1-58113-912-9 OR 9781581139129
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: link analysis; search engines; web information retrieval
acknowledgement: ack-nhfb
rawauthor: Andrei Z. Broder and Ronny Lempel and Farzin Maghoul and Jan Pedersen
raweditor: {ACM}
rawtitle: Efficient {PageRank} approximation via graph aggregation
booktitle:
International World Wide Web Conference Proceedings of the 13th international World Wide Web conference: Alternate track papers & posters

abstract:
We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. We (1) partition the Web's graph into classes of quasi-equivalent vertices, (2) project the page-based random walk to be approximated onto those classes, and (3) compute the stationary probability distribution of the resulting class-based random walk. From this distribution we can quickly reconstruct a distribution on pages. In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. We experimented on a Web-graph containing over 1.4 billion pages, and were able to produce a ranking that has Spearman rank-order correlation of 0.95 with respect to PageRank. A simplistic implementation of our method required less than half the running time of a highly optimized implementation of PageRank, implying that larger speedup factors are probably possible.

rawabstract:
We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. We (1) partition the Web's graph into classes of quasi-equivalent vertices, (2) project the page-based random walk to be approximated onto those classes, and (3) compute the stationary probability distribution of the resulting class-based random walk. From this distribution we can quickly reconstruct a distribution on pages. In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. We experimented on a Web-graph containing over 1.4 billion pages, and were able to produce a ranking that has Spearman rank-order correlation of 0.95 with respect to PageRank. A simplistic implementation of our method required less than half the running time of a highly optimized implementation of PageRank, implying that larger speedup factors are probably possible.

rawbooktitle:
International World Wide Web Conference Proceedings of the 13th international World Wide Web conference: Alternate track papers \& posters

entry:
@InProceedings{Broder:2004:EPA,
  author =       "Andrei Z. Broder and Ronny Lempel and Farzin Maghoul
                 and Jan Pedersen",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 13th international World Wide Web conference:
                 Alternate track papers \& posters",
  title =        "Efficient {PageRank} approximation via graph
                 aggregation",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "484--485",
  year =         "2004",
  DOI =          "https://doi.org/10.1145/1013367.1013537",
  ISBN =         "1-58113-912-8",
  ISBN-13 =      "978-1-58113-912-9",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We present a framework for approximating random-walk
                 based probability distributions over Web pages using
                 graph aggregation. We (1) partition the Web's graph
                 into classes of quasi-equivalent vertices, (2) project
                 the page-based random walk to be approximated onto
                 those classes, and (3) compute the stationary
                 probability distribution of the resulting class-based
                 random walk. From this distribution we can quickly
                 reconstruct a distribution on pages. In particular, our
                 framework can approximate the well-known PageRank
                 distribution by setting the classes according to the
                 set of pages on each Web host. We experimented on a
                 Web-graph containing over 1.4 billion pages, and were
                 able to produce a ranking that has Spearman rank-order
                 correlation of 0.95 with respect to PageRank. A
                 simplistic implementation of our method required less
                 than half the running time of a highly optimized
                 implementation of PageRank, implying that larger
                 speedup factors are probably possible.",
  acknowledgement = ack-nhfb,
  keywords =     "link analysis; search engines; web information
                 retrieval",
}


bibtype: inproceedings
filename: pagerank.bib
label: Chen:2004:LME
author: Yen-Yu Chen and Qingqing Gan and Torsten Suel
editor: ACM
title: Local methods for estimating PageRank values
publisher: pub-ACM
address: pub-ACM:adr
pages: 381--389
year: 2004
DOI: https://doi.org/10.1145/383952.384003
ISBN: 1-58113-874-1 OR 1581138741
ISBN13: 978-1-58113-874-0 OR 9781581138740
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
acknowledgement: ack-nhfb
rawauthor: Yen-Yu Chen and Qingqing Gan and Torsten Suel
raweditor: {ACM}
rawtitle: Local methods for estimating {PageRank} values
booktitle:
Conference on Information and Knowledge Management Proceedings of the thirteenth ACM international conference on Information and knowledge management

abstract:
The Google search engine uses a method called PageRank, together with term-based and other ranking techniques, to order search results returned to the user. PageRank uses link analysis to assign a global importance score to each web page. The PageRank scores of all the pages are usually determined off-line in a large-scale computation on the entire hyperlink graph of the web, and several recent studies have focused on improving the efficiency of this computation, which may require multiple hours on a workstation. par However, in some scenarios, such as online analysis of link evolution and mining of large web archives such as the Internet Archive, it may be desirable to quickly approximate or update the PageRanks of individual nodes without performing a large-scale computation on the entire graph. We address this problem by studying several methods for efficiently estimating the PageRank score of a particular web page using only a small subgraph of the entire web. In our model, we assume that the graph is accessible remotely via a link database (such as the AltaVista Connectivity Server) or is stored in a relational database that performs lookups on disks to retrieve node and connectivity information. We show that a reasonable estimate of the PageRank value of a node is possible in most cases by retrieving only a moderate number of nodes in the local neighborhood of the node.

keywords:
external memory algorithms; link database; link-based ranking; out-of-core; pagerank; search engines

rawabstract:
The Google search engine uses a method called PageRank, together with term-based and other ranking techniques, to order search results returned to the user. PageRank uses link analysis to assign a global importance score to each web page. The PageRank scores of all the pages are usually determined off-line in a large-scale computation on the entire hyperlink graph of the web, and several recent studies have focused on improving the efficiency of this computation, which may require multiple hours on a workstation. \par However, in some scenarios, such as online analysis of link evolution and mining of large web archives such as the Internet Archive, it may be desirable to quickly approximate or update the PageRanks of individual nodes without performing a large-scale computation on the entire graph. We address this problem by studying several methods for efficiently estimating the PageRank score of a particular web page using only a small subgraph of the entire web. In our model, we assume that the graph is accessible remotely via a link database (such as the AltaVista Connectivity Server) or is stored in a relational database that performs lookups on disks to retrieve node and connectivity information. We show that a reasonable estimate of the PageRank value of a node is possible in most cases by retrieving only a moderate number of nodes in the local neighborhood of the node.

rawbooktitle:
Conference on Information and Knowledge Management Proceedings of the thirteenth ACM international conference on Information and knowledge management

entry:
@InProceedings{Chen:2004:LME,
  author =       "Yen-Yu Chen and Qingqing Gan and Torsten Suel",
  editor =       "{ACM}",
  booktitle =    "Conference on Information and Knowledge Management
                 Proceedings of the thirteenth ACM international
                 conference on Information and knowledge management",
  title =        "Local methods for estimating {PageRank} values",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "381--389",
  year =         "2004",
  DOI =          "https://doi.org/10.1145/383952.384003",
  ISBN =         "1-58113-874-1",
  ISBN-13 =      "978-1-58113-874-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The Google search engine uses a method called
                 PageRank, together with term-based and other ranking
                 techniques, to order search results returned to the
                 user. PageRank uses link analysis to assign a global
                 importance score to each web page. The PageRank scores
                 of all the pages are usually determined off-line in a
                 large-scale computation on the entire hyperlink graph
                 of the web, and several recent studies have focused on
                 improving the efficiency of this computation, which may
                 require multiple hours on a workstation. \par

                 However, in some scenarios, such as online analysis of
                 link evolution and mining of large web archives such as
                 the Internet Archive, it may be desirable to quickly
                 approximate or update the PageRanks of individual nodes
                 without performing a large-scale computation on the
                 entire graph. We address this problem by studying
                 several methods for efficiently estimating the PageRank
                 score of a particular web page using only a small
                 subgraph of the entire web. In our model, we assume
                 that the graph is accessible remotely via a link
                 database (such as the AltaVista Connectivity Server) or
                 is stored in a relational database that performs
                 lookups on disks to retrieve node and connectivity
                 information. We show that a reasonable estimate of the
                 PageRank value of a node is possible in most cases by
                 retrieving only a moderate number of nodes in the local
                 neighborhood of the node.",
  acknowledgement = ack-nhfb,
  keywords =     "external memory algorithms; link database; link-based
                 ranking; out-of-core; pagerank; search engines",
}


bibtype: inproceedings
filename: pagerank.bib
label: Chirita:2004:FRP
author: P. Chirita and D. Olmedilla and W. Nejdl
title: Finding Related Pages Using the Link Structure of the WWW
crossref: Zhong:2004:IWS
pages: 632--635
year: 2004
DOI: https://doi.org/10.1109/WI.2004.10056
bibdate: Thu May 06 14:07:03 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 14:07:03 ???
acknowledgement: ack-nhfb
rawauthor: P. Chirita and D. Olmedilla and W. Nejdl
rawtitle: Finding Related Pages Using the Link Structure of the {WWW}
entry:
@InProceedings{Chirita:2004:FRP,
  author =       "P. Chirita and D. Olmedilla and W. Nejdl",
  title =        "Finding Related Pages Using the Link Structure of the
                 {WWW}",
  crossref =     "Zhong:2004:IWS",
  pages =        "632--635",
  year =         "2004",
  DOI =          "https://doi.org/10.1109/WI.2004.10056",
  bibdate =      "Thu May 06 14:07:03 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Ding:2004:LAH
author: Chris H. Q. Ding and Hongyuan Zha and Xiaofeng He and Parry Husbands and Horst D. Simon
title: Link Analysis: Hubs and Authorities on the World Wide Web
journal: j-SIAM-REVIEW
volume: 46
number: 2
pages: 256--268
month: jun
monthnumber: 6
year: 2004
CODEN: SIREAD
DOI: https://doi.org/10.1137/S0036144501389218
ISSN: 0036-1445 (print), 1095-7200 (electronic) OR 00361445 (print), 10957200 (electronic)
ISSNL: 0036-1445 OR 00361445
bibdate: Sat Apr 16 12:47:29 MDT 2005
bibtimestamp: 2005.04.16 12:47:29 MDT
URL: http://epubs.siam.org/sam-bin/dbq/article/38921
fjournal: SIAM Review
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sirev
rawauthor: Chris H. Q. Ding and Hongyuan Zha and Xiaofeng He and Parry Husbands and Horst D. Simon
rawtitle: Link Analysis: Hubs and Authorities on the {World Wide Web}
bibsource:
http://epubs.siam.org/sam-bin/dbq/toc/SIREV/46/2; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Ding:2004:LAH,
  author =       "Chris H. Q. Ding and Hongyuan Zha and Xiaofeng He and
                 Parry Husbands and Horst D. Simon",
  title =        "Link Analysis: Hubs and Authorities on the {World Wide
                 Web}",
  journal =      j-SIAM-REVIEW,
  volume =       "46",
  number =       "2",
  pages =        "256--268",
  month =        jun,
  year =         "2004",
  CODEN =        "SIREAD",
  DOI =          "https://doi.org/10.1137/S0036144501389218",
  ISSN =         "0036-1445 (print), 1095-7200 (electronic)",
  ISSN-L =       "0036-1445",
  bibdate =      "Sat Apr 16 12:47:29 MDT 2005",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SIREV/46/2;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://epubs.siam.org/sam-bin/dbq/article/38921",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Review",
  journal-URL =  "http://epubs.siam.org/sirev",
}


bibtype: techreport
filename: pagerank.bib
label: Gleich:2004:FPP
author: D. Gleich and L. Zhukov and P. Berkhin
title: Fast Parallel PageRank: a Linear System Approach
type: Technical Report
number: YRL-2004-038
institution: Yahoo! Research
pages: 256--268
year: 2004
bibdate: Wed Nov 30 08:08:31 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.11.30 08:08:31 ???
URL: http:research.yahoo.com/publication/YRL-2004-035.pdf
acknowledgement: ack-nhfb
rawauthor: D. Gleich and L. Zhukov and P. Berkhin
rawtitle: Fast Parallel {PageRank}: a Linear System Approach
entry:
@TechReport{Gleich:2004:FPP,
  author =       "D. Gleich and L. Zhukov and P. Berkhin",
  title =        "Fast Parallel {PageRank}: a Linear System Approach",
  type =         "Technical Report",
  number =       "YRL-2004-038",
  institution =  "Yahoo! Research",
  address =      "????",
  year =         "2004",
  bibdate =      "Wed Nov 30 08:08:31 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http:research.yahoo.com/publication/YRL-2004-035.pdf",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Gyongyi:2004:CWS
author: Z. Gyongyi and H. Garcia-Molina and J. Pedersen
title: Combating web spam with TrustRank
publisher: pub-MORGAN-KAUFMANN
address: pub-MORGAN-KAUFMANN:adr
pages: 576--587
year: 2004
DOI: https://doi.org/10.1016/B978-012088469-8.50052-8
LCCN: QA76.9.D3 I559 2004
bibdate: Tue Aug 11 17:00:09 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:00:09 ???
URL: http://www.sciencedirect.com/science/article/pii/B9780120884698500528
acknowledgement: ack-nhfb
bookURL: http://www.sciencedirect.com/science/book/9780120884698
bookpages: 1380
rawauthor: Z. Gy{\"o}ngyi and H. Garcia-Molina and J. Pedersen
rawtitle: Combating web spam with {TrustRank}
editor:
Mario A. Nascimento and M. Tamer Ozsu and Donald Kossmann and Renee J. Miller and Jose A. Blakeley and K. Bernhard Schiefer

booktitle:
Proceedings of the Thirtieth International Conference on Very Large Data Bases: VLDB '04. Toronto, Canada, Aug. 31--Sept. 3, 2004

ISBN:
0-12-088469-0 (paperback), 0-12-722442-4, 0-08-053979-3 (e-book) OR 0120884690 (paperback), 0127224424, 0080539793 (ebook)

ISBN13:
978-0-12-088469-8 (paperback), 978-0-12-722442-8, 978-0-08-053979-9 (e-book) OR 9780120884698 (paperback), 9780127224428, 9780080539799 (ebook)

raweditor:
Mario A. Nascimento and M. Tamer {\"O}zsu and Donald Kossmann and Ren{\'e}e J. Miller and Jos{\'e} A. Blakeley and K. Bernhard Schiefer

entry:
@InProceedings{Gyongyi:2004:CWS,
  author =       "Z. Gy{\"o}ngyi and H. Garcia-Molina and J. Pedersen",
  editor =       "Mario A. Nascimento and M. Tamer {\"O}zsu and Donald
                 Kossmann and Ren{\'e}e J. Miller and Jos{\'e} A.
                 Blakeley and K. Bernhard Schiefer",
  booktitle =    "Proceedings of the Thirtieth International Conference
                 on Very Large Data Bases: VLDB '04. Toronto, Canada,
                 Aug. 31--Sept. 3, 2004",
  title =        "Combating web spam with {TrustRank}",
  publisher =    pub-MORGAN-KAUFMANN,
  address =      pub-MORGAN-KAUFMANN:adr,
  pages =        "576--587",
  year =         "2004",
  DOI =          "https://doi.org/10.1016/B978-012088469-8.50052-8",
  ISBN =         "0-12-088469-0 (paperback), 0-12-722442-4,
                 0-08-053979-3 (e-book)",
  ISBN-13 =      "978-0-12-088469-8 (paperback), 978-0-12-722442-8,
                 978-0-08-053979-9 (e-book)",
  LCCN =         "QA76.9.D3 I559 2004",
  bibdate =      "Tue Aug 11 17:00:09 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/B9780120884698500528",
  acknowledgement = ack-nhfb,
  book-URL =     "http://www.sciencedirect.com/science/book/9780120884698",
  bookpages =    "1380",
}


bibtype: inproceedings
filename: pagerank.bib
label: Ingongngam:2004:TCA
author: P. Ingongngam and A. Rungsawang
title: Topic-centric algorithm: a novel approach to Web link analysis
crossref: Barolli:2004:ICA
pages: 299--301
year: 2004
DOI: https://doi.org/10.1109/AINA.2004.1283807
bibdate: Thu May 06 14:11:27 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 14:11:27 ???
acknowledgement: ack-nhfb
rawauthor: P. Ingongngam and A. Rungsawang
rawtitle: Topic-centric algorithm: a novel approach to {Web} link analysis
entry:
@InProceedings{Ingongngam:2004:TCA,
  author =       "P. Ingongngam and A. Rungsawang",
  title =        "Topic-centric algorithm: a novel approach to {Web}
                 link analysis",
  crossref =     "Barolli:2004:ICA",
  pages =        "299--301",
  year =         "2004",
  DOI =          "https://doi.org/10.1109/AINA.2004.1283807",
  bibdate =      "Thu May 06 14:11:27 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: misc
filename: pagerank.bib
label: Kamvar:2004:ACR
author: Sepandar D. Kamvar and Taher H. Haveliwala and Gene H. Golub
title: Adaptive computation of ranking
pages: 299--301
day: 23
month: aug
monthnumber: 8
year: 2004
bibdate: Wed Jun 01 18:43:31 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.01 18:43:31 ???
URL: http://patft.uspto.gov/netahtml/PTO/srchnum.htm
acknowledgement: ack-nhfb
howpublished: US Patent 7,028,029.
rawauthor: Sepandar D. Kamvar and Taher H. Haveliwala and Gene H. Golub
rawtitle: Adaptive computation of ranking
abstract:
A system and method is disclosed in which a ranking function for a set of document rank values is iteratively solved with respect to a set of linked documents until a first stability condition is satisfied. After such condition is satisfied, some of the ranks will have converged. The ranking function is modified to take into account these converged ranks so as to reduce the ranking function's computation cost. The modified ranking function is then solved until a second stability condition is satisfied. After such condition is satisfied more of the ranks will have converged. The ranking function is again modified and process continues until complete.

rawabstract:
A system and method is disclosed in which a ranking function for a set of document rank values is iteratively solved with respect to a set of linked documents until a first stability condition is satisfied. After such condition is satisfied, some of the ranks will have converged. The ranking function is modified to take into account these converged ranks so as to reduce the ranking function's computation cost. The modified ranking function is then solved until a second stability condition is satisfied. After such condition is satisfied more of the ranks will have converged. The ranking function is again modified and process continues until complete.

entry:
@Misc{Kamvar:2004:ACR,
  author =       "Sepandar D. Kamvar and Taher H. Haveliwala and Gene H.
                 Golub",
  title =        "Adaptive computation of ranking",
  howpublished = "US Patent 7,028,029.",
  day =          "23",
  month =        aug,
  year =         "2004",
  bibdate =      "Wed Jun 01 18:43:31 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://patft.uspto.gov/netahtml/PTO/srchnum.htm",
  abstract =     "A system and method is disclosed in which a ranking
                 function for a set of document rank values is
                 iteratively solved with respect to a set of linked
                 documents until a first stability condition is
                 satisfied. After such condition is satisfied, some of
                 the ranks will have converged. The ranking function is
                 modified to take into account these converged ranks so
                 as to reduce the ranking function's computation cost.
                 The modified ranking function is then solved until a
                 second stability condition is satisfied. After such
                 condition is satisfied more of the ranks will have
                 converged. The ranking function is again modified and
                 process continues until complete.",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Kamvar:2004:AMC
author: Sepandar Kamvar and Taher Haveliwala and Gene Golub
title: Adaptive methods for the computation of PageRank
journal: j-LINEAR-ALGEBRA-APPL
volume: 386
number: 1
pages: 51--65
day: 15
month: jul
monthnumber: 7
year: 2004
CODEN: LAAPAW
DOI: https://doi.org/10.1016/j.laa.2003.12.008
ISSN: 0024-3795 (print), 1873-1856 (electronic) OR 00243795 (print), 18731856 (electronic)
ISSNL: 0024-3795 OR 00243795
MRclass: 60-04 (60G50 60J10)
MRnumber: MR2066607
bibdate: Tue Nov 9 07:02:36 MST 2004
bibtimestamp: 2004.11.09 07:02:36 MST
fjournal: Linear Algebra and its Applications
keywords: Google search engine; PageRank algorithm
ZMnumber: 1091.68044
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00243795
rawauthor: Sepandar Kamvar and Taher Haveliwala and Gene Golub
rawtitle: Adaptive methods for the computation of {PageRank}
bibsource:
https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/00243795

URL:
https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib; https://www.math.utah.edu/pub/tex/bib/linala2000.bib

entry:
@Article{Kamvar:2004:AMC,
  author =       "Sepandar Kamvar and Taher Haveliwala and Gene Golub",
  title =        "Adaptive methods for the computation of {PageRank}",
  journal =      j-LINEAR-ALGEBRA-APPL,
  volume =       "386",
  number =       "1",
  pages =        "51--65",
  day =          "15",
  month =        jul,
  year =         "2004",
  CODEN =        "LAAPAW",
  DOI =          "https://doi.org/10.1016/j.laa.2003.12.008",
  ISSN =         "0024-3795 (print), 1873-1856 (electronic)",
  ISSN-L =       "0024-3795",
  MRclass =      "60-04 (60G50 60J10)",
  MRnumber =     "MR2066607",
  bibdate =      "Tue Nov 9 07:02:36 MST 2004",
  bibsource =    "https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00243795",
  URL =          "https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
                 https://www.math.utah.edu/pub/tex/bib/linala2000.bib",
  ZMnumber =     "1091.68044",
  acknowledgement = ack-nhfb,
  fjournal =     "Linear Algebra and its Applications",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00243795",
  keywords =     "Google search engine; PageRank algorithm",
}


bibtype: article
filename: pagerank.bib
label: Langville:2004:DIP
author: Amy N. Langville and Carl D. Meyer
title: Deeper inside PageRank
journal: j-INTERNET-MATH
volume: 1
number: 3
pages: 335--380
year: 2004
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68U35
MRnumber: MR2111012
bibdate: Wed May 5 19:27:49 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:27:49 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1109190965
fjournal: Internet Mathematics
ZMnumber: 1098.68010
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Amy N. Langville and Carl D. Meyer
rawtitle: Deeper inside {PageRank}
entry:
@Article{Langville:2004:DIP,
  author =       "Amy N. Langville and Carl D. Meyer",
  title =        "Deeper inside {PageRank}",
  journal =      j-INTERNET-MATH,
  volume =       "1",
  number =       "3",
  pages =        "335--380",
  year =         "2004",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35",
  MRnumber =     "MR2111012",
  bibdate =      "Wed May 5 19:27:49 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1109190965",
  ZMnumber =     "1098.68010",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: inproceedings
filename: pagerank.bib
label: Langville:2004:UPI
author: Amy Nicole Langville and Carl Dean Meyer
editor: ACM
booktitle: Proceedings of the 13th international World Wide Web conference: Alternate track papers & posters
title: Updating PageRank with iterative aggregation
publisher: pub-ACM
address: pub-ACM:adr
pages: 392--393
year: 2004
DOI: https://doi.org/10.1137/1031050
ISBN: 1-58113-912-8 OR 1581139128
ISBN13: 978-1-58113-912-9 OR 9781581139129
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
acknowledgement: ack-nhfb
rawauthor: Amy Nicole Langville and Carl Dean Meyer
raweditor: {ACM}
rawtitle: Updating {PageRank} with iterative aggregation
abstract:
We present an algorithm for updating the PageRank vector [1]. Due to the scale of the web, Google only updates its famous PageRank vector on a monthly basis. However, the Web changes much more frequently. Drastically speeding the PageRank computation can lead to fresher, more accurate rankings of the webpages retrieved by search engines. It can also make the goal of real-time personalized rankings within reach. On two small subsets of the web, our algorithm updates PageRank using just 25% and 14%, respectively, of the time required by the original PageRank algorithm. Our algorithm uses iterative aggregation techniques [7, 8] to focus on the slow-converging states of the Markov chain. The most exciting feature of this algorithm is that it can be joined with other PageRank acceleration methods, such as the dangling node lumpability algorithm [6], quadratic extrapolation [4], and adaptive PageRank [3], to realize even greater speedups (potentially a factor of 60 or more speedup when all algorithms are combined). every few weeks. Our solution harnesses the power of iterative aggregation principles for Markov chains to allow for much more frequent updates to the valuable ranking vectors.

keywords:
aggregation; disaggregation; link analysis; Markov chains; pagerank; power method; stationary vector; updating

rawabstract:
We present an algorithm for updating the PageRank vector [1]. Due to the scale of the web, Google only updates its famous PageRank vector on a monthly basis. However, the Web changes much more frequently. Drastically speeding the PageRank computation can lead to fresher, more accurate rankings of the webpages retrieved by search engines. It can also make the goal of real-time personalized rankings within reach. On two small subsets of the web, our algorithm updates PageRank using just 25\% and 14\%, respectively, of the time required by the original PageRank algorithm. Our algorithm uses iterative aggregation techniques [7, 8] to focus on the slow-converging states of the Markov chain. The most exciting feature of this algorithm is that it can be joined with other PageRank acceleration methods, such as the dangling node lumpability algorithm [6], quadratic extrapolation [4], and adaptive PageRank [3], to realize even greater speedups (potentially a factor of 60 or more speedup when all algorithms are combined). every few weeks. Our solution harnesses the power of iterative aggregation principles for Markov chains to allow for much more frequent updates to the valuable ranking vectors.

rawbooktitle:
{Proceedings of the 13th international World Wide Web conference: Alternate track papers \& posters}

entry:
@InProceedings{Langville:2004:UPI,
  author =       "Amy Nicole Langville and Carl Dean Meyer",
  editor =       "{ACM}",
  booktitle =    "{Proceedings of the 13th international World Wide Web
                 conference: Alternate track papers \& posters}",
  title =        "Updating {PageRank} with iterative aggregation",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "392--393",
  year =         "2004",
  DOI =          "https://doi.org/10.1137/1031050",
  ISBN =         "1-58113-912-8",
  ISBN-13 =      "978-1-58113-912-9",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We present an algorithm for updating the PageRank
                 vector [1]. Due to the scale of the web, Google only
                 updates its famous PageRank vector on a monthly basis.
                 However, the Web changes much more frequently.
                 Drastically speeding the PageRank computation can lead
                 to fresher, more accurate rankings of the webpages
                 retrieved by search engines. It can also make the goal
                 of real-time personalized rankings within reach. On two
                 small subsets of the web, our algorithm updates
                 PageRank using just 25\% and 14\%, respectively, of the
                 time required by the original PageRank algorithm. Our
                 algorithm uses iterative aggregation techniques [7, 8]
                 to focus on the slow-converging states of the Markov
                 chain. The most exciting feature of this algorithm is
                 that it can be joined with other PageRank acceleration
                 methods, such as the dangling node lumpability
                 algorithm [6], quadratic extrapolation [4], and
                 adaptive PageRank [3], to realize even greater speedups
                 (potentially a factor of 60 or more speedup when all
                 algorithms are combined). every few weeks. Our solution
                 harnesses the power of iterative aggregation principles
                 for Markov chains to allow for much more frequent
                 updates to the valuable ranking vectors.",
  acknowledgement = ack-nhfb,
  keywords =     "aggregation; disaggregation; link analysis; Markov
                 chains; pagerank; power method; stationary vector;
                 updating",
}


bibtype: inproceedings
filename: pagerank.bib
label: Manaskasemsak:2004:PPC
author: Bundit Manaskasemsak and Arnon Rungsawang
title: Parallel PageRank computation on a gigabit PC cluster
crossref: Barolli:2004:ICA
volume: 1
pages: 273--277
year: 2004
DOI: https://doi.org/10.1109/AINA.2004.1283923
bibdate: Fri Feb 19 18:16:05 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 18:16:05 ???
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1283923
acknowledgement: ack-nhfb
rawauthor: Bundit Manaskasemsak and Arnon Rungsawang
rawtitle: Parallel {PageRank} computation on a gigabit {PC} cluster
abstract:
Efficient computing the PageRank scores for a large web graph is actually one of the hot issues in Web-IR community. Recent researches propose to accelerate the computation, both in algorithmic and architectural ways. We here focus on a parallel PageRank computational architecture on a cluster of Opteron PCs networked via a Gigabit Ethernet. We propose both an efficient parallel algorithm of the standard PageRank computation, and a simple pairwise communication model needed to synchronize local PageRank scores between processors. Our experimental results conducted on a large web graph, over 1.5 billion links, synthesized from the real set of crawled web pages in the TH domain, are quite promising. The current implementation takes less than15 seconds for an iteration run.

rawabstract:
Efficient computing the PageRank scores for a large web graph is actually one of the hot issues in Web-IR community. Recent researches propose to accelerate the computation, both in algorithmic and architectural ways. We here focus on a parallel PageRank computational architecture on a cluster of Opteron PCs networked via a Gigabit Ethernet. We propose both an efficient parallel algorithm of the standard PageRank computation, and a simple pairwise communication model needed to synchronize local PageRank scores between processors. Our experimental results conducted on a large web graph, over 1.5 billion links, synthesized from the real set of crawled web pages in the TH domain, are quite promising. The current implementation takes less than15 seconds for an iteration run.

entry:
@InProceedings{Manaskasemsak:2004:PPC,
  author =       "Bundit Manaskasemsak and Arnon Rungsawang",
  title =        "Parallel {PageRank} computation on a gigabit {PC}
                 cluster",
  crossref =     "Barolli:2004:ICA",
  volume =       "1",
  pages =        "273--277",
  year =         "2004",
  DOI =          "https://doi.org/10.1109/AINA.2004.1283923",
  bibdate =      "Fri Feb 19 18:16:05 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1283923",
  abstract =     "Efficient computing the PageRank scores for a large
                 web graph is actually one of the hot issues in Web-IR
                 community. Recent researches propose to accelerate the
                 computation, both in algorithmic and architectural
                 ways. We here focus on a parallel PageRank
                 computational architecture on a cluster of Opteron PCs
                 networked via a Gigabit Ethernet. We propose both an
                 efficient parallel algorithm of the standard PageRank
                 computation, and a simple pairwise communication model
                 needed to synchronize local PageRank scores between
                 processors. Our experimental results conducted on a
                 large web graph, over 1.5 billion links, synthesized
                 from the real set of crawled web pages in the TH
                 domain, are quite promising. The current implementation
                 takes less than15 seconds for an iteration run.",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Markarian:2004:IEN
author: Roberto Markarian and Nelson Moller
title: The importance of each node in a structure of links: Google PageRank
journal: Bol. Asoc. Mat. Venez.
volume: 11
number: 2
pages: 233--252
year: 2004
ISSN: 1315-4125 OR 13154125
MRclass: 68U35 (15A18)
MRnumber: MR2139430
bibdate: Wed May 5 19:27:59 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:27:59 ???
fjournal: Boleti n de la Asociacion Matematica Venezolana
ZMnumber: 1086.68658
acknowledgement: ack-nhfb
rawauthor: Roberto Markarian and Nelson M{\"o}ller
rawtitle: The importance of each node in a structure of links: {Google PageRank}
entry:
@Article{Markarian:2004:IEN,
  author =       "Roberto Markarian and Nelson M{\"o}ller",
  title =        "The importance of each node in a structure of links:
                 {Google PageRank}",
  journal =      "Bol. Asoc. Mat. Venez.",
  volume =       "11",
  number =       "2",
  pages =        "233--252",
  year =         "2004",
  CODEN =        "????",
  ISSN =         "1315-4125",
  MRclass =      "68U35 (15A18)",
  MRnumber =     "MR2139430",
  bibdate =      "Wed May 5 19:27:59 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1086.68658",
  acknowledgement = ack-nhfb,
  fjournal =     "Bolet\'\i n de la Asociaci\'on Matem\'atica
                 Venezolana",
}


bibtype: inproceedings
filename: pagerank.bib
label: Meng:2004:ELA
author: Tao Meng and Hongfei Yan and Jimin Wang and Xiaoming Li
title: The Evolution of Link-Attributes for Pages and Its Implications on Web Crawling
crossref: Zhong:2004:IWS
pages: 578--581
year: 2004
bibdate: Thu May 06 14:14:46 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 14:14:46 ???
acknowledgement: ack-nhfb
rawauthor: Tao Meng and Hongfei Yan and Jimin Wang and Xiaoming Li
rawtitle: The Evolution of Link-Attributes for Pages and Its Implications on Web Crawling
entry:
@InProceedings{Meng:2004:ELA,
  author =       "Tao Meng and Hongfei Yan and Jimin Wang and Xiaoming
                 Li",
  title =        "The Evolution of Link-Attributes for Pages and Its
                 Implications on Web Crawling",
  crossref =     "Zhong:2004:IWS",
  pages =        "578--581",
  year =         "2004",
  bibdate =      "Thu May 06 14:14:46 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Mihalcea:2004:PSN
author: Rada Mihalcea and Paul Tarau and Elizabeth Figa
editor: Margaret King and others
title: PageRank on semantic networks, with application to word sense disambiguation
publisher: Association for Computational Linguistics
address: Morristown, NJ, USA
pages: ??--??
year: 2004
DOI: https://doi.org/10.3115/1220355.1220517
ISBN: 1-932432-48-5 OR 1932432485
ISBN13: 978-1-932432-48-0 OR 9781932432480
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
URL: http://www.cs.unt.edu/~rada/papers/mihalcea.coling04.pdf
acknowledgement: ack-nhfb
bookpages: xvi + 763
rawauthor: Rada Mihalcea and Paul Tarau and Elizabeth Figa
raweditor: Margaret King and others
rawtitle: {PageRank} on semantic networks, with application to word sense disambiguation
booktitle:
Coling Geneva 2004: 20th International Conference on Computational Linguistics, August 23rd to 27th, 2004: proceedings

abstract:
This paper presents a new open text word sense disambiguation method that combines the use of logical inferences with PageRank-style algorithms applied on graphs extracted from natural language documents. We evaluate the accuracy of the proposed algorithm on several sense-annotated texts, and show that it consistently outperforms the accuracy of other previously proposed knowledge-based word sense disambiguation methods. We also explore and evaluate methods that combine several open-text word sense disambiguation algorithms.

rawabstract:
This paper presents a new open text word sense disambiguation method that combines the use of logical inferences with PageRank-style algorithms applied on graphs extracted from natural language documents. We evaluate the accuracy of the proposed algorithm on several sense-annotated texts, and show that it consistently outperforms the accuracy of other previously proposed knowledge-based word sense disambiguation methods. We also explore and evaluate methods that combine several open-text word sense disambiguation algorithms.

rawbooktitle:
{Coling Geneva 2004: 20th International Conference on Computational Linguistics, August 23rd to 27th, 2004: proceedings}

entry:
@InProceedings{Mihalcea:2004:PSN,
  author =       "Rada Mihalcea and Paul Tarau and Elizabeth Figa",
  editor =       "Margaret King and others",
  booktitle =    "{Coling Geneva 2004: 20th International Conference on
                 Computational Linguistics, August 23rd to 27th, 2004:
                 proceedings}",
  title =        "{PageRank} on semantic networks, with application to
                 word sense disambiguation",
  publisher =    "Association for Computational Linguistics",
  address =      "Morristown, NJ, USA",
  pages =        "??--??",
  year =         "2004",
  DOI =          "https://doi.org/10.3115/1220355.1220517",
  ISBN =         "1-932432-48-5",
  ISBN-13 =      "978-1-932432-48-0",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.cs.unt.edu/~rada/papers/mihalcea.coling04.pdf",
  abstract =     "This paper presents a new open text word sense
                 disambiguation method that combines the use of logical
                 inferences with PageRank-style algorithms applied on
                 graphs extracted from natural language documents. We
                 evaluate the accuracy of the proposed algorithm on
                 several sense-annotated texts, and show that it
                 consistently outperforms the accuracy of other
                 previously proposed knowledge-based word sense
                 disambiguation methods. We also explore and evaluate
                 methods that combine several open-text word sense
                 disambiguation algorithms.",
  acknowledgement = ack-nhfb,
  bookpages =    "xvi + 763",
  pagecount =    "7",
}


bibtype: inproceedings
filename: pagerank.bib
label: Suzuki:2004:HDP
author: K. Suzuki
title: How does propagational investment currency system change the world?
crossref: IEEE:2004:SWI
pages: 9--15
year: 2004
DOI: https://doi.org/10.1109/SAINTW.2004.1268559
bibdate: Thu May 06 14:02:50 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 14:02:50 ???
acknowledgement: ack-nhfb
rawauthor: K. Suzuki
rawtitle: How does propagational investment currency system change the world?
entry:
@InProceedings{Suzuki:2004:HDP,
  author =       "K. Suzuki",
  title =        "How does propagational investment currency system
                 change the world?",
  crossref =     "IEEE:2004:SWI",
  pages =        "9--15",
  year =         "2004",
  DOI =          "https://doi.org/10.1109/SAINTW.2004.1268559",
  bibdate =      "Thu May 06 14:02:50 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Upstill:2003:PFF
author: Trystan Upstill and Nick Craswell and David Hawking
title: Predicting fame and fortune: PageRank or Indegree?
pages: 31--40
month: dec
monthnumber: 12
year: 2003
bibdate: Mon Jul 08 08:43:33 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2019.07.08 08:43:33 ???
acknowledgement: ack-nhfb
rawauthor: Trystan Upstill and Nick Craswell and David Hawking
rawtitle: Predicting fame and fortune: {PageRank} or {Indegree}?
booktitle:
Proceedings of the 8th Australasian Document Computing Symposium, Canberra, Australia, December 15, 2003 (ADCS 2003)

rawbooktitle:
{Proceedings of the 8th Australasian Document Computing Symposium, Canberra, Australia, December 15, 2003 (ADCS 2003)}

entry:
@InProceedings{Upstill:2003:PFF,
  author =       "Trystan Upstill and Nick Craswell and David Hawking",
  editor =       "????",
  booktitle =    "{Proceedings of the 8th Australasian Document
                 Computing Symposium, Canberra, Australia, December 15,
                 2003 (ADCS 2003)}",
  title =        "Predicting fame and fortune: {PageRank} or
                 {Indegree}?",
  publisher =    "????",
  address =      "????",
  pages =        "31--40",
  month =        dec,
  year =         "2003",
  bibdate =      "Mon Jul 08 08:43:33 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Wang:2004:CPD
author: Yuan Wang and David J. DeWitt
editor: Mario A. Nascimento
title: Computing PageRank in a distributed Internet search system
volume: 30
publisher: pub-MORGAN-KAUFMANN
address: pub-MORGAN-KAUFMANN:adr
pages: 420--431
year: 2004
DOI: https://doi.org/10.1145/383059.383071
ISBN: 0-12-088469-0 OR 0120884690
ISBN13: 978-0-12-088469-8 OR 9780120884698
LCCN: QA76.9.D3 I559 2004
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
acknowledgement: ack-nhfb
rawauthor: Yuan Wang and David J. DeWitt
raweditor: Mario A. Nascimento
rawtitle: Computing {PageRank} in a distributed {Internet} search system
booktitle:
Proceedings of the thirtieth International Conference on Very Large Data Bases: Toronto, Canada, August 31--September 3, 2004

abstract:
Existing Internet search engines use web crawlers to download data from the Web. Page quality is measured on central servers, where user queries are also processed. This paper argues that using crawlers has a list of disadvantages. Most importantly, crawlers do not scale. Even Google, the leading search engine, indexes less than 1% of the entire Web. This paper proposes a distributed search engine framework, in which every web server answers queries over its own data. Results from multiple web servers will be merged to generate a ranked hyperlink list on the submitting server. This paper presents a series of algorithms that compute PageRank in such framework. The preliminary experiments on a real data set demonstrate that the system achieves comparable accuracy on PageRank vectors to Google's well-known PageRank algorithm and, therefore, high quality of query results.

rawabstract:
Existing Internet search engines use web crawlers to download data from the Web. Page quality is measured on central servers, where user queries are also processed. This paper argues that using crawlers has a list of disadvantages. Most importantly, crawlers do not scale. Even Google, the leading search engine, indexes less than 1\% of the entire Web. This paper proposes a distributed search engine framework, in which every web server answers queries over its own data. Results from multiple web servers will be merged to generate a ranked hyperlink list on the submitting server. This paper presents a series of algorithms that compute PageRank in such framework. The preliminary experiments on a real data set demonstrate that the system achieves comparable accuracy on PageRank vectors to Google's well-known PageRank algorithm and, therefore, high quality of query results.

rawbooktitle:
{Proceedings of the thirtieth International Conference on Very Large Data Bases: Toronto, Canada, August 31--September 3, 2004}

entry:
@InProceedings{Wang:2004:CPD,
  author =       "Yuan Wang and David J. DeWitt",
  editor =       "Mario A. Nascimento",
  booktitle =    "{Proceedings of the thirtieth International Conference
                 on Very Large Data Bases: Toronto, Canada, August
                 31--September 3, 2004}",
  title =        "Computing {PageRank} in a distributed {Internet}
                 search system",
  volume =       "30",
  publisher =    pub-MORGAN-KAUFMANN,
  address =      pub-MORGAN-KAUFMANN:adr,
  pages =        "420--431",
  year =         "2004",
  DOI =          "https://doi.org/10.1145/383059.383071",
  ISBN =         "0-12-088469-0",
  ISBN-13 =      "978-0-12-088469-8",
  LCCN =         "QA76.9.D3 I559 2004",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Existing Internet search engines use web crawlers to
                 download data from the Web. Page quality is measured on
                 central servers, where user queries are also processed.
                 This paper argues that using crawlers has a list of
                 disadvantages. Most importantly, crawlers do not scale.
                 Even Google, the leading search engine, indexes less
                 than 1\% of the entire Web. This paper proposes a
                 distributed search engine framework, in which every web
                 server answers queries over its own data. Results from
                 multiple web servers will be merged to generate a
                 ranked hyperlink list on the submitting server. This
                 paper presents a series of algorithms that compute
                 PageRank in such framework. The preliminary experiments
                 on a real data set demonstrate that the system achieves
                 comparable accuracy on PageRank vectors to Google's
                 well-known PageRank algorithm and, therefore, high
                 quality of query results.",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Xing:2004:WPA
author: W. Xing and A. Ghorbani
booktitle: Proceedings of the Second Annual Conference on Communication Networks and Services Research (2004)
title: Weighted PageRank Algorithm
crossref: Ghorbani:2004:PAC
pages: 305--314
year: 2004
DOI: https://doi.org/10.1109/DNSR.2004.1344743
ISBN: 0-7695-2096-0 OR 0769520960
ISBN13: 978-0-7695-2096-4 OR 9780769520964
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=1344743
keywords: HITS; PageRank; Web Mining; Web Structure Mining; Weighted PageRank
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9316
rawauthor: W. Xing and A. Ghorbani
rawtitle: Weighted {PageRank} Algorithm
abstract:
With the rapid growth of the Web, users get easily lost in the rich hyper structure. Providing relevant information to the users to cater to their needs is the primary goal of website owners. Therefore, finding the content of the Web and retrieving the users' interests and needs from their behavior have become increasingly important. Web mining is used to categorize users and pages by analyzing the users' behavior,the content of the pages, and the order of the URLs that tend to be accessed in order. Web structure mining plays an important role in this approach. Two page ranking algorithms, HITS and PageRank, are commonly used in web structure mining. Both algorithms treat all links equally when distributing rank scores. Several algorithms have been developed to improve the performance of these methods. The Weighted PageRank algorithm (WPR), an extension to the standard PageRank algorithm, is introduced in this paper. WPR takes into account the importance of both the inlinks and the outlinks of the pages and distributes rank scores based on the popularity of the pages. The results of our simulation studies show that WPR performs better than the conventional PageRank algorithm in terms of returning larger number of relevant pages to a given query.

rawabstract:
With the rapid growth of the Web, users get easily lost in the rich hyper structure. Providing relevant information to the users to cater to their needs is the primary goal of website owners. Therefore, finding the content of the Web and retrieving the users' interests and needs from their behavior have become increasingly important. Web mining is used to categorize users and pages by analyzing the users' behavior,the content of the pages, and the order of the URLs that tend to be accessed in order. Web structure mining plays an important role in this approach. Two page ranking algorithms, HITS and PageRank, are commonly used in web structure mining. Both algorithms treat all links equally when distributing rank scores. Several algorithms have been developed to improve the performance of these methods. The Weighted PageRank algorithm (WPR), an extension to the standard PageRank algorithm, is introduced in this paper. WPR takes into account the importance of both the inlinks and the outlinks of the pages and distributes rank scores based on the popularity of the pages. The results of our simulation studies show that WPR performs better than the conventional PageRank algorithm in terms of returning larger number of relevant pages to a given query.

rawbooktitle:
{Proceedings of the Second Annual Conference on Communication Networks and Services Research (2004)}

entry:
@InProceedings{Xing:2004:WPA,
  author =       "W. Xing and A. Ghorbani",
  booktitle =    "{Proceedings of the Second Annual Conference on
                 Communication Networks and Services Research (2004)}",
  title =        "Weighted {PageRank} Algorithm",
  crossref =     "Ghorbani:2004:PAC",
  pages =        "305--314",
  year =         "2004",
  DOI =          "https://doi.org/10.1109/DNSR.2004.1344743",
  ISBN =         "0-7695-2096-0",
  ISBN-13 =      "978-0-7695-2096-4",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1344743",
  abstract =     "With the rapid growth of the Web, users get easily
                 lost in the rich hyper structure. Providing relevant
                 information to the users to cater to their needs is the
                 primary goal of website owners. Therefore, finding the
                 content of the Web and retrieving the users' interests
                 and needs from their behavior have become increasingly
                 important. Web mining is used to categorize users and
                 pages by analyzing the users' behavior,the content of
                 the pages, and the order of the URLs that tend to be
                 accessed in order. Web structure mining plays an
                 important role in this approach. Two page ranking
                 algorithms, HITS and PageRank, are commonly used in web
                 structure mining. Both algorithms treat all links
                 equally when distributing rank scores. Several
                 algorithms have been developed to improve the
                 performance of these methods. The Weighted PageRank
                 algorithm (WPR), an extension to the standard PageRank
                 algorithm, is introduced in this paper. WPR takes into
                 account the importance of both the inlinks and the
                 outlinks of the pages and distributes rank scores based
                 on the popularity of the pages. The results of our
                 simulation studies show that WPR performs better than
                 the conventional PageRank algorithm in terms of
                 returning larger number of relevant pages to a given
                 query.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9316",
  keywords =     "HITS; PageRank; Web Mining; Web Structure Mining;
                 Weighted PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Yamamoto:2004:DPD
author: A. Yamamoto and D. Asahara and T. Itao and S. Tanaka and T. Suda
title: Distributed PageRank: a distributed reputation model for open peer-to-peer network
crossref: IEEE:2004:SWI
pages: 389--394
year: 2004
DOI: https://doi.org/10.1109/SAINTW.2004.1268664
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1268664
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8957
rawauthor: A. Yamamoto and D. Asahara and T. Itao and S. Tanaka and T. Suda
rawtitle: Distributed {PageRank}: a distributed reputation model for open peer-to-peer network
abstract:
This paper proposes a distributed reputation model for open peer-to-peer networks called distributed pagerank. This model is motivated by the observation that although pagerank has already satisfied the requirements of reputation models, the centralized calculation of pagerank is incompatible with peer-to-peer networks. Distributed pagerank is a decentralized approach for calculating the pagerank of each peer by its reputation, in which the relationship between peers is introduced as the equivalent to the link between web pages. The distributed calculation of pagerank is performed asynchronously by each peer as it communicates with the other peers. The asynchronous calculation accomplishes both demanding no extra messages for the calculation of pagerank and steadily calculating an accurate pagerank of each peer even under the dynamic topology of relationships. The result of the simulation has indicated that the calculated pagerank value of each peer converges at the original pagerank value under the static topology of relationships, which is presumable under a dynamic topology. A fully implemented application of distributed pagerank has also been presented, which supports dynamic formation of communities with reputation ranking.

rawabstract:
This paper proposes a distributed reputation model for open peer-to-peer networks called distributed pagerank. This model is motivated by the observation that although pagerank has already satisfied the requirements of reputation models, the centralized calculation of pagerank is incompatible with peer-to-peer networks. Distributed pagerank is a decentralized approach for calculating the pagerank of each peer by its reputation, in which the relationship between peers is introduced as the equivalent to the link between web pages. The distributed calculation of pagerank is performed asynchronously by each peer as it communicates with the other peers. The asynchronous calculation accomplishes both demanding no extra messages for the calculation of pagerank and steadily calculating an accurate pagerank of each peer even under the dynamic topology of relationships. The result of the simulation has indicated that the calculated pagerank value of each peer converges at the original pagerank value under the static topology of relationships, which is presumable under a dynamic topology. A fully implemented application of distributed pagerank has also been presented, which supports dynamic formation of communities with reputation ranking.

entry:
@InProceedings{Yamamoto:2004:DPD,
  author =       "A. Yamamoto and D. Asahara and T. Itao and S. Tanaka
                 and T. Suda",
  title =        "Distributed {PageRank}: a distributed reputation model
                 for open peer-to-peer network",
  crossref =     "IEEE:2004:SWI",
  pages =        "389--394",
  year =         "2004",
  DOI =          "https://doi.org/10.1109/SAINTW.2004.1268664",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1268664",
  abstract =     "This paper proposes a distributed reputation model for
                 open peer-to-peer networks called distributed pagerank.
                 This model is motivated by the observation that
                 although pagerank has already satisfied the
                 requirements of reputation models, the centralized
                 calculation of pagerank is incompatible with
                 peer-to-peer networks. Distributed pagerank is a
                 decentralized approach for calculating the pagerank of
                 each peer by its reputation, in which the relationship
                 between peers is introduced as the equivalent to the
                 link between web pages. The distributed calculation of
                 pagerank is performed asynchronously by each peer as it
                 communicates with the other peers. The asynchronous
                 calculation accomplishes both demanding no extra
                 messages for the calculation of pagerank and steadily
                 calculating an accurate pagerank of each peer even
                 under the dynamic topology of relationships. The result
                 of the simulation has indicated that the calculated
                 pagerank value of each peer converges at the original
                 pagerank value under the static topology of
                 relationships, which is presumable under a dynamic
                 topology. A fully implemented application of
                 distributed pagerank has also been presented, which
                 supports dynamic formation of communities with
                 reputation ranking.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8957",
}


bibtype: inbook
filename: pagerank.bib
label: Altman:2005:RSPa
editor: Alon Altman
title: Ranking systems: the PageRank axioms
publisher: International Begegnungs- und Forschungszentrum fur Informatik
address: Wadern, Germany
pages: ??--??
year: 2005
bibdate: Fri Jun 3 10:03:23 MDT 2011
bibsource: fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 10:03:23 MDT
series: Dagstuhl seminar proceedings 05011
URL: http://drops.dagstuhl.de/opus/volltexte/2005/197/pdf/05011.AltmanAlon.Paper
acknowledgement: ack-nhfb
raweditor: Alon Altman
rawtitle: Ranking systems: the {PageRank} axioms
entry:
@InBook{Altman:2005:RSPa,
  editor =       "Alon Altman",
  title =        "Ranking systems: the {PageRank} axioms",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "??--??",
  year =         "2005",
  ISBN =         "????",
  ISBN-13 =      "????",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings 05011",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2005/197/pdf/05011.AltmanAlon.Paper",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Altman:2005:RSPb
author: A. Altman and M. Tennenholtz
title: Ranking systems: the PageRank axioms
publisher: pub-ACM
address: pub-ACM:adr
pages: 1--8
year: 2005
ISBN: 1-59593-049-3 OR 1595930493
ISBN13: 978-1-59593-049-1 OR 9781595930491
LCCN: HF5548.32 .A26 2005
bibdate: Tue Jul 20 16:00:08 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.07.20 16:00:08 ???
keywords: PageRank
acknowledgement: ack-nhfb
bookpages: viii + 294
rawauthor: A. Altman and M. Tennenholtz
rawtitle: Ranking systems: the {PageRank} axioms
booktitle:
EC '05: proceedings of the 6th ACM Conference on Electronic Commerce, Vancouver, Canada, June 5--8, 2005

rawbooktitle:
{EC '05: proceedings of the 6th ACM Conference on Electronic Commerce, Vancouver, Canada, June 5--8, 2005}

entry:
@InProceedings{Altman:2005:RSPb,
  author =       "A. Altman and M. Tennenholtz",
  booktitle =    "{EC '05: proceedings of the 6th ACM Conference on
                 Electronic Commerce, Vancouver, Canada, June 5--8,
                 2005}",
  title =        "Ranking systems: the {PageRank} axioms",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "1--8",
  year =         "2005",
  ISBN =         "1-59593-049-3",
  ISBN-13 =      "978-1-59593-049-1",
  LCCN =         "HF5548.32 .A26 2005",
  bibdate =      "Tue Jul 20 16:00:08 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "viii + 294",
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Benczur:2005:FLR
author: Andras A. Benczur and Karoly Csalogany and Tamas Sarlos
editor: ACM
booktitle: Special interest tracks and posters of the 14th international conference on World Wide Web
title: On the feasibility of low-rank approximation for personalized PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 972--973
year: 2005
DOI: https://doi.org/10.1145/1062745.1062824
ISBN: 1-59593-051-5 OR 1595930515
ISBN13: 978-1-59593-051-4 OR 9781595930514
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
acknowledgement: ack-nhfb
rawauthor: Andr{\'a}s A. Bencz{\'u}r and K{\'a}roly Csalog{\'a}ny and Tam{\'a}s Sarl{\'o}s
rawbooktitle: {Special interest tracks and posters of the 14th international conference on World Wide Web}
raweditor: {ACM}
rawtitle: On the feasibility of low-rank approximation for personalized {PageRank}
abstract:
Personalized PageRank expresses backlink-based page quality around user-selected pages in a similar way to PageRank over the entire Web. Algorithms for computing personalized PageRank on the fly are either limited to a restricted choice of page selection or believed to behave well only on sparser regions of the Web. In this paper we show the feasibility of computing personalized PageRank by a $ k < 1000 $ low-rank approximation of the Page-Rank transition matrix; by our algorithm we may compute an approximate personalized Page-Rank by multiplying an $ n times k $, a $ k times n $ matrix and the $n$-dimensional personalization vector. Since low-rank approximations are accurate on dense regions, we hope that our technique will combine well with known algorithms.

keywords:
link analysis; low-rank approximation; personalized PageRank; singular value decomposition; web information retrieval

rawabstract:
Personalized PageRank expresses backlink-based page quality around user-selected pages in a similar way to PageRank over the entire Web. Algorithms for computing personalized PageRank on the fly are either limited to a restricted choice of page selection or believed to behave well only on sparser regions of the Web. In this paper we show the feasibility of computing personalized PageRank by a $ k < 1000 $ low-rank approximation of the Page-Rank transition matrix; by our algorithm we may compute an approximate personalized Page-Rank by multiplying an $ n \times k $, a $ k \times n $ matrix and the $n$-dimensional personalization vector. Since low-rank approximations are accurate on dense regions, we hope that our technique will combine well with known algorithms.

entry:
@InProceedings{Benczur:2005:FLR,
  author =       "Andr{\'a}s A. Bencz{\'u}r and K{\'a}roly Csalog{\'a}ny
                 and Tam{\'a}s Sarl{\'o}s",
  editor =       "{ACM}",
  booktitle =    "{Special interest tracks and posters of the 14th
                 international conference on World Wide Web}",
  title =        "On the feasibility of low-rank approximation for
                 personalized {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "972--973",
  year =         "2005",
  DOI =          "https://doi.org/10.1145/1062745.1062824",
  ISBN =         "1-59593-051-5",
  ISBN-13 =      "978-1-59593-051-4",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Personalized PageRank expresses backlink-based page
                 quality around user-selected pages in a similar way to
                 PageRank over the entire Web. Algorithms for computing
                 personalized PageRank on the fly are either limited to
                 a restricted choice of page selection or believed to
                 behave well only on sparser regions of the Web. In this
                 paper we show the feasibility of computing personalized
                 PageRank by a $ k < 1000 $ low-rank approximation of
                 the Page-Rank transition matrix; by our algorithm we
                 may compute an approximate personalized Page-Rank by
                 multiplying an $ n \times k $, a $ k \times n $ matrix
                 and the $n$-dimensional personalization vector. Since
                 low-rank approximations are accurate on dense regions,
                 we hope that our technique will combine well with known
                 algorithms.",
  acknowledgement = ack-nhfb,
  keywords =     "link analysis; low-rank approximation; personalized
                 PageRank; singular value decomposition; web information
                 retrieval",
}


bibtype: article
filename: pagerank.bib
label: Berkhin:2005:SPC
author: Pavel Berkhin
title: A survey on PageRank computing
journal: j-INTERNET-MATH
volume: 2
number: 1
pages: 73--120
year: 2005
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68U35
MRnumber: MR2166277 (2006c:68180)
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1128530802
fjournal: Internet Mathematics
ZMnumber: 1100.68504
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Pavel Berkhin
rawtitle: A survey on {PageRank} computing
entry:
@Article{Berkhin:2005:SPC,
  author =       "Pavel Berkhin",
  title =        "A survey on {PageRank} computing",
  journal =      j-INTERNET-MATH,
  volume =       "2",
  number =       "1",
  pages =        "73--120",
  year =         "2005",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35",
  MRnumber =     "MR2166277 (2006c:68180)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1128530802",
  ZMnumber =     "1100.68504",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: book
filename: pagerank.bib
label: Berry:2005:USE
author: Michael W. Berry and Murray Browne
title: Understanding search engines: mathematical modeling and text retrieval
publisher: pub-SIAM
address: pub-SIAM:adr
edition: Second
pages: xvii + 117
year: 2005
ISBN: 0-89871-581-4 OR 0898715814
ISBN13: 978-0-89871-581-1 OR 9780898715811
LCCN: TK5105.884 .B47 2005
bibdate: Fri Jun 3 10:03:23 MDT 2011
bibsource: fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 10:03:23 MDT
subject: Web search engines; Vector spaces; Text processing (Computer science)
acknowledgement: ack-nhfb
rawauthor: Michael W. Berry and Murray Browne
rawtitle: Understanding search engines: mathematical modeling and text retrieval
TOC:
Preface to the Second Edition 1. Introduction 1.1. Document file preparation 1.1.1. Manual indexing 1.1.2. File cleanup 1.2. Information extraction 1.3. Vector space modeling 1.4. Matrix decompositions 1.5. Query representations 1.6. Ranking and relevance feedback 1.7. Searching by link structure 1.8. User interface 1.9. Book format 2. Document file preparation 2.1. Document purification and analysis 2.1.1. Text formatting 2.1.2. Validation 2.2. Manual indexing 2.3. Automatic indexing 2.4. Item normalization 2.5. Inverted file structures 2.5.1. Document file 2.5.2. Dictionary list 2.5.3. Inversion list 2.5.4. Other file structures 3. Vector space models 3.1. Construction 3.1.1. Term-by-document matrices 3.1.2. Simple Query matching 3.2. Design issues 3.2.1. Term weighting 3.2.2. Sparse matrix storage 3.2.3. Low-rank approximations 4. Matrix decompositions 4.1. QR factorization 4.2. Singular value decomposition 4.2.1. Low-rank approximations 4.2.2. Query matching 4.2.3. Software 4.3. Semidiscrete decomposition 4.4. Updating techniques 5. Query management 5.1. Query binding 5.2. Types of queries 5.2.1. Boolean queries 5.2.2. Natural language queries 5.2.3. Thesaurus queries 5.2.4. Fuzzy queries 5.2.5. Term searches 5.2.6. Probabilistic queries 6. Ranking and relevance feedback 6.1. Performance evaluation 6.1.1. Precision 6.1.2. Recall 6.1.3. Average precision 6.1.4. Genetic algorithms 6.2. Relevance feedback 7. Searching by link structure 7.1. HITS method 7.1.1. HITS implementation 7.1.2. HITS summary 7.2. PageRank method 7.2.1. PageRank adjustments 7.2.2. PageRank implementation 7.2.3. PageRank summary 8. User interface considerations 8.1. General guidelines 8.2. Search engine interfaces 8.2.1. Form fill-in 8.2.2. Display considerations 8.2.3. Progress indication 8.2.4. No penalties for error 8.2.5. Results 8.2.6. Test and retest 8.2.7. Final considerations 9. Further reading 9.1. General textbooks on IR 9.2. Computational methods and software 9.3. Search engines 9.4. User interfaces Bibliography Index

rawTOC:
Preface to the Second Edition \\ 1. Introduction \\ 1.1. Document file preparation \\ 1.1.1. Manual indexing \\ 1.1.2. File cleanup \\ 1.2. Information extraction \\ 1.3. Vector space modeling \\ 1.4. Matrix decompositions \\ 1.5. Query representations \\ 1.6. Ranking and relevance feedback \\ 1.7. Searching by link structure \\ 1.8. User interface \\ 1.9. Book format \\ 2. Document file preparation \\ 2.1. Document purification and analysis \\ 2.1.1. Text formatting \\ 2.1.2. Validation \\ 2.2. Manual indexing \\ 2.3. Automatic indexing \\ 2.4. Item normalization \\ 2.5. Inverted file structures \\ 2.5.1. Document file \\ 2.5.2. Dictionary list \\ 2.5.3. Inversion list \\ 2.5.4. Other file structures \\ 3. Vector space models \\ 3.1. Construction \\ 3.1.1. Term-by-document matrices \\ 3.1.2. Simple Query matching \\ 3.2. Design issues \\ 3.2.1. Term weighting \\ 3.2.2. Sparse matrix storage \\ 3.2.3. Low-rank approximations \\ 4. Matrix decompositions \\ 4.1. QR factorization \\ 4.2. Singular value decomposition \\ 4.2.1. Low-rank approximations \\ 4.2.2. Query matching \\ 4.2.3. Software \\ 4.3. Semidiscrete decomposition \\ 4.4. Updating techniques \\ 5. Query management \\ 5.1. Query binding \\ 5.2. Types of queries \\ 5.2.1. Boolean queries \\ 5.2.2. Natural language queries \\ 5.2.3. Thesaurus queries \\ 5.2.4. Fuzzy queries \\ 5.2.5. Term searches \\ 5.2.6. Probabilistic queries \\ 6. Ranking and relevance feedback \\ 6.1. Performance evaluation \\ 6.1.1. Precision \\ 6.1.2. Recall \\ 6.1.3. Average precision \\ 6.1.4. Genetic algorithms \\ 6.2. Relevance feedback \\ 7. Searching by link structure \\ 7.1. HITS method \\ 7.1.1. HITS implementation \\ 7.1.2. HITS summary \\ 7.2. PageRank method \\ 7.2.1. PageRank adjustments \\ 7.2.2. PageRank implementation \\ 7.2.3. PageRank summary \\ 8. User interface considerations \\ 8.1. General guidelines \\ 8.2. Search engine interfaces \\ 8.2.1. Form fill-in \\ 8.2.2. Display considerations \\ 8.2.3. Progress indication \\ 8.2.4. No penalties for error \\ 8.2.5. Results \\ 8.2.6. Test and retest \\ 8.2.7. Final considerations \\ 9. Further reading \\ 9.1. General textbooks on IR \\ 9.2. Computational methods and software \\ 9.3. Search engines \\ 9.4. User interfaces \\ Bibliography \\ Index

entry:
@Book{Berry:2005:USE,
  author =       "Michael W. Berry and Murray Browne",
  title =        "Understanding search engines: mathematical modeling
                 and text retrieval",
  publisher =    pub-SIAM,
  address =      pub-SIAM:adr,
  edition =      "Second",
  pages =        "xvii + 117",
  year =         "2005",
  ISBN =         "0-89871-581-4",
  ISBN-13 =      "978-0-89871-581-1",
  LCCN =         "TK5105.884 .B47 2005",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  subject =      "Web search engines; Vector spaces; Text processing
                 (Computer science)",
  tableofcontents = "Preface to the Second Edition \\
                 1. Introduction \\
                 1.1. Document file preparation \\
                 1.1.1. Manual indexing \\
                 1.1.2. File cleanup \\
                 1.2. Information extraction \\
                 1.3. Vector space modeling \\
                 1.4. Matrix decompositions \\
                 1.5. Query representations \\
                 1.6. Ranking and relevance feedback \\
                 1.7. Searching by link structure \\
                 1.8. User interface \\
                 1.9. Book format \\
                 2. Document file preparation \\
                 2.1. Document purification and analysis \\
                 2.1.1. Text formatting \\
                 2.1.2. Validation \\
                 2.2. Manual indexing \\
                 2.3. Automatic indexing \\
                 2.4. Item normalization \\
                 2.5. Inverted file structures \\
                 2.5.1. Document file \\
                 2.5.2. Dictionary list \\
                 2.5.3. Inversion list \\
                 2.5.4. Other file structures \\
                 3. Vector space models \\
                 3.1. Construction \\
                 3.1.1. Term-by-document matrices \\
                 3.1.2. Simple Query matching \\
                 3.2. Design issues \\
                 3.2.1. Term weighting \\
                 3.2.2. Sparse matrix storage \\
                 3.2.3. Low-rank approximations \\
                 4. Matrix decompositions \\
                 4.1. QR factorization \\
                 4.2. Singular value decomposition \\
                 4.2.1. Low-rank approximations \\
                 4.2.2. Query matching \\
                 4.2.3. Software \\
                 4.3. Semidiscrete decomposition \\
                 4.4. Updating techniques \\
                 5. Query management \\
                 5.1. Query binding \\
                 5.2. Types of queries \\
                 5.2.1. Boolean queries \\
                 5.2.2. Natural language queries \\
                 5.2.3. Thesaurus queries \\
                 5.2.4. Fuzzy queries \\
                 5.2.5. Term searches \\
                 5.2.6. Probabilistic queries \\
                 6. Ranking and relevance feedback \\
                 6.1. Performance evaluation \\
                 6.1.1. Precision \\
                 6.1.2. Recall \\
                 6.1.3. Average precision \\
                 6.1.4. Genetic algorithms \\
                 6.2. Relevance feedback \\
                 7. Searching by link structure \\
                 7.1. HITS method \\
                 7.1.1. HITS implementation \\
                 7.1.2. HITS summary \\
                 7.2. PageRank method \\
                 7.2.1. PageRank adjustments \\
                 7.2.2. PageRank implementation \\
                 7.2.3. PageRank summary \\
                 8. User interface considerations \\
                 8.1. General guidelines \\
                 8.2. Search engine interfaces \\
                 8.2.1. Form fill-in \\
                 8.2.2. Display considerations \\
                 8.2.3. Progress indication \\
                 8.2.4. No penalties for error \\
                 8.2.5. Results \\
                 8.2.6. Test and retest \\
                 8.2.7. Final considerations \\
                 9. Further reading \\
                 9.1. General textbooks on IR \\
                 9.2. Computational methods and software \\
                 9.3. Search engines \\
                 9.4. User interfaces \\
                 Bibliography \\
                 Index",
}


bibtype: article
filename: pagerank.bib
label: Bianchini:2005:IP
author: Monica Bianchini and Marco Gori and Franco Scarselli
title: Inside PageRank
journal: j-TOIT
volume: 5
number: 1
pages: 92--128
month: feb
monthnumber: 2
year: 2005
DOI: https://doi.org/10.1016/S0169-7552(98)00061-0
ISSN: 1533-5399 (print), 1557-6051 (electronic) OR 15335399 (print), 15576051 (electronic)
ISSNL: 1533-5399 OR 15335399
bibdate: Thu Apr 14 10:31:40 MDT 2005
bibsource: http://www.acm.org/pubs/contents/journals/toit/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2005.04.14 10:31:40 MDT
URL: https://www.math.utah.edu/pub/tex/bib/toit.bib
fjournal: ACM Transactions on Internet Technology (TOIT)
keywords: Information retrieval; Markov chains; PageRank; search engines; searching the Web; Web page scoring
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J780
rawauthor: Monica Bianchini and Marco Gori and Franco Scarselli
rawtitle: Inside {PageRank}
abstract:
Although the interest of a Web page is strictly related to its content and to the subjective readers' cultural background, a measure of the page authority can be provided that only depends on the topological structure of the Web. PageRank is a noticeable way to attach a score to Web pages on the basis of the Web connectivity. In this article, we look inside PageRank to disclose its fundamental properties concerning stability, complexity of computational scheme, and critical role of parameters involved in the computation. Moreover, we introduce a circuit analysis that allows us to understand the distribution of the page score, the way different Web communities interact each other, the role of dangling pages (pages with no outlinks), and the secrets for promotion of Web pages.

rawabstract:
Although the interest of a Web page is strictly related to its content and to the subjective readers' cultural background, a measure of the page authority can be provided that only depends on the topological structure of the Web. PageRank is a noticeable way to attach a score to Web pages on the basis of the Web connectivity. In this article, we look inside PageRank to disclose its fundamental properties concerning stability, complexity of computational scheme, and critical role of parameters involved in the computation. Moreover, we introduce a circuit analysis that allows us to understand the distribution of the page score, the way different Web communities interact each other, the role of dangling pages (pages with no outlinks), and the secrets for promotion of Web pages.

entry:
@Article{Bianchini:2005:IP,
  author =       "Monica Bianchini and Marco Gori and Franco Scarselli",
  title =        "Inside {PageRank}",
  journal =      j-TOIT,
  volume =       "5",
  number =       "1",
  pages =        "92--128",
  month =        feb,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1016/S0169-7552(98)00061-0",
  ISSN =         "1533-5399 (print), 1557-6051 (electronic)",
  ISSN-L =       "1533-5399",
  bibdate =      "Thu Apr 14 10:31:40 MDT 2005",
  bibsource =    "http://www.acm.org/pubs/contents/journals/toit/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://www.math.utah.edu/pub/tex/bib/toit.bib",
  abstract =     "Although the interest of a Web page is strictly
                 related to its content and to the subjective readers'
                 cultural background, a measure of the page authority
                 can be provided that only depends on the topological
                 structure of the Web. PageRank is a noticeable way to
                 attach a score to Web pages on the basis of the Web
                 connectivity. In this article, we look inside PageRank
                 to disclose its fundamental properties concerning
                 stability, complexity of computational scheme, and
                 critical role of parameters involved in the
                 computation. Moreover, we introduce a circuit analysis
                 that allows us to understand the distribution of the
                 page score, the way different Web communities interact
                 each other, the role of dangling pages (pages with no
                 outlinks), and the secrets for promotion of Web
                 pages.",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM Transactions on Internet Technology (TOIT)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J780",
  keywords =     "Information retrieval; Markov chains; PageRank; search
                 engines; searching the Web; Web page scoring",
}


bibtype: article
filename: pagerank.bib
label: Boldi:2005:PEP
author: Paolo Boldi and Massimo Santini and Sebastiano Vigna
title: Paradoxical effects in PageRank incremental computations
journal: j-INTERNET-MATH
volume: 2
number: 3
pages: 387--404
year: 2005
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68U35 (05C80 68R10)
MRnumber: MR2212371 (2006j:68129)
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1150474888
fjournal: Internet Mathematics
ZMnumber: 1095.68503
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Paolo Boldi and Massimo Santini and Sebastiano Vigna
rawtitle: Paradoxical effects in {PageRank} incremental computations
entry:
@Article{Boldi:2005:PEP,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
  title =        "Paradoxical effects in {PageRank} incremental
                 computations",
  journal =      j-INTERNET-MATH,
  volume =       "2",
  number =       "3",
  pages =        "387--404",
  year =         "2005",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C80 68R10)",
  MRnumber =     "MR2212371 (2006j:68129)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1150474888",
  ZMnumber =     "1095.68503",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: inproceedings
filename: pagerank.bib
label: Boldi:2005:PFD
author: Paolo Boldi and Massimo Santini and Sebastiano Vigna
editor: ACM
title: PageRank as a function of the damping factor
publisher: pub-ACM
address: pub-ACM:adr
pages: 557--566
year: 2005
DOI: https://doi.org/10.1145/382979.383041
ISBN: 1-59593-046-9 OR 1595930469
ISBN13: 978-1-59593-046-0 OR 9781595930460
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: approximation; PageRank; Web graph
acknowledgement: ack-nhfb
rawauthor: Paolo Boldi and Massimo Santini and Sebastiano Vigna
raweditor: {ACM}
rawtitle: {PageRank} as a function of the damping factor
booktitle:
International World Wide Web Conference Proceedings of the 14th international conference on World Wide Web

abstract:
PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor $ alpha $ that spreads uniformly part of the rank. The choice of $ alpha $ is eminently empirical, and in most cases the original suggestion $ alpha $ = 0.85 by Brin and Page is still used. Recently, however, the behaviour of PageRank with respect to changes in $ alpha $ was discovered to be useful in link-spam detection[21]. Moreover, an analytical justification of the value chosen for $ alpha $ is still missing. In this paper, we give the first mathematical analysis of PageRank when $ alpha $ changes. In particular, we show that, contrarily to popular belief, for real-world graphs values of $ alpha $ close to 1 do not give a more meaningful ranking. Then, we give closed-form formulae for PageRank derivatives of any order, and an extension of the Power Method that approximates them with convergence O (t k $ alpha $ t ) for the k-th derivative. Finally, we show a tight connection between iterated computation and analytical behaviour by proving that the k-th iteration of the Power Method gives exactly the PageRank value obtained using a Maclaurin polynomial of degree k. The latter result paves the way towards the application of analytical methods to the study of PageRank.

rawabstract:
PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor $ \alpha $ that spreads uniformly part of the rank. The choice of $ \alpha $ is eminently empirical, and in most cases the original suggestion $ \alpha $ = 0.85 by Brin and Page is still used. Recently, however, the behaviour of PageRank with respect to changes in $ \alpha $ was discovered to be useful in link-spam detection[21]. Moreover, an analytical justification of the value chosen for $ \alpha $ is still missing. In this paper, we give the first mathematical analysis of PageRank when $ \alpha $ changes. In particular, we show that, contrarily to popular belief, for real-world graphs values of $ \alpha $ close to 1 do not give a more meaningful ranking. Then, we give closed-form formulae for PageRank derivatives of any order, and an extension of the Power Method that approximates them with convergence O (t k $ \alpha $ t ) for the k-th derivative. Finally, we show a tight connection between iterated computation and analytical behaviour by proving that the k-th iteration of the Power Method gives exactly the PageRank value obtained using a Maclaurin polynomial of degree k. The latter result paves the way towards the application of analytical methods to the study of PageRank.

rawbooktitle:
International World Wide Web Conference Proceedings of the 14th international conference on World Wide Web

entry:
@InProceedings{Boldi:2005:PFD,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 14th international conference on World Wide Web",
  title =        "{PageRank} as a function of the damping factor",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "557--566",
  year =         "2005",
  DOI =          "https://doi.org/10.1145/382979.383041",
  ISBN =         "1-59593-046-9",
  ISBN-13 =      "978-1-59593-046-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "PageRank is defined as the stationary state of a
                 Markov chain. The chain is obtained by perturbing the
                 transition matrix induced by a web graph with a damping
                 factor $ \alpha $ that spreads uniformly part of the
                 rank. The choice of $ \alpha $ is eminently empirical,
                 and in most cases the original suggestion $ \alpha $ =
                 0.85 by Brin and Page is still used. Recently, however,
                 the behaviour of PageRank with respect to changes in $
                 \alpha $ was discovered to be useful in link-spam
                 detection[21]. Moreover, an analytical justification of
                 the value chosen for $ \alpha $ is still missing. In
                 this paper, we give the first mathematical analysis of
                 PageRank when $ \alpha $ changes. In particular, we
                 show that, contrarily to popular belief, for real-world
                 graphs values of $ \alpha $ close to 1 do not give a
                 more meaningful ranking. Then, we give closed-form
                 formulae for PageRank derivatives of any order, and an
                 extension of the Power Method that approximates them
                 with convergence O (t k $ \alpha $ t ) for the k-th
                 derivative. Finally, we show a tight connection between
                 iterated computation and analytical behaviour by
                 proving that the k-th iteration of the Power Method
                 gives exactly the PageRank value obtained using a
                 Maclaurin polynomial of degree k. The latter result
                 paves the way towards the application of analytical
                 methods to the study of PageRank.",
  acknowledgement = ack-nhfb,
  keywords =     "approximation; PageRank; Web graph",
}


bibtype: inproceedings
filename: pagerank.bib
label: Boldi:2005:TRD
author: P. Boldi
booktitle: Poster Proceedings of the 14th International Conference on the World Wide Web (WWW2005)
title: TotalRank: Ranking without damping
publisher: pub-ACM
address: pub-ACM:adr
pages: 898--899
year: 2005
bibdate: Tue Aug 11 17:28:42 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:28:42 ???
acknowledgement: ack-nhfb
rawauthor: P. Boldi
rawbooktitle: Poster Proceedings of the 14th International Conference on the World Wide Web (WWW2005)
rawtitle: {TotalRank}: Ranking without damping
entry:
@InProceedings{Boldi:2005:TRD,
  author =       "P. Boldi",
  booktitle =    "Poster Proceedings of the 14th International
                 Conference on the World Wide Web (WWW2005)",
  title =        "{TotalRank}: Ranking without damping",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "898--899",
  year =         "2005",
  bibdate =      "Tue Aug 11 17:28:42 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Brezinski:2005:EMP
author: Claude Brezinski and Michela Redivo-Zaglia and Stefano Serra-Capizzano
title: Extrapolation methods for PageRank computations
journal: j-C-R-MATH-ACAD-SCI-PARIS
volume: 340
number: 5
pages: 393--397
year: 2005
DOI: https://doi.org/10.1016/j.crma.2005.01.015
ISSN: 1631-073X OR 1631073X
ISSNL: 1631-073X OR 1631073X
MRclass: 65F15
MRnumber: MR2127117
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
fjournal: Comptes Rendus Mathematique. Academie des Sciences. Paris
ZMnumber: 1066.65040
acknowledgement: ack-nhfb
rawauthor: Claude Brezinski and Michela Redivo-Zaglia and Stefano Serra-Capizzano
rawtitle: Extrapolation methods for {PageRank} computations
entry:
@Article{Brezinski:2005:EMP,
  author =       "Claude Brezinski and Michela Redivo-Zaglia and Stefano
                 Serra-Capizzano",
  title =        "Extrapolation methods for {PageRank} computations",
  journal =      j-C-R-MATH-ACAD-SCI-PARIS,
  volume =       "340",
  number =       "5",
  pages =        "393--397",
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1016/j.crma.2005.01.015",
  ISSN =         "1631-073X",
  ISSN-L =       "1631-073X",
  MRclass =      "65F15",
  MRnumber =     "MR2127117",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1066.65040",
  acknowledgement = ack-nhfb,
  fjournal =     "Comptes Rendus Math\'ematique. Acad\'emie des
                 Sciences. Paris",
}


bibtype: inproceedings
filename: pagerank.bib
label: daCosta:2005:WSM
author: M. G. da Costa, Jr. and Zhiguo Gong
title: Web structure mining: an introduction
crossref: Meng:2005:IIC
pages: ??--??
year: 2005
DOI: https://doi.org/10.1109/ICIA.2005.1635156
bibdate: Thu May 06 15:33:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:33:04 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: M. G. {da Costa, Jr.} and Zhiguo Gong
rawtitle: {Web} structure mining: an introduction
entry:
@InProceedings{daCosta:2005:WSM,
  author =       "M. G. {da Costa, Jr.} and Zhiguo Gong",
  title =        "{Web} structure mining: an introduction",
  crossref =     "Meng:2005:IIC",
  pages =        "??--??",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/ICIA.2005.1635156",
  bibdate =      "Thu May 06 15:33:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: techreport
filename: pagerank.bib
label: DelCorso:2005:CKS
author: Gianna M. Del Corso and Antonio Gulli and Francesco Romani
title: Comparison of Krylov Subspace Methods on the PageRank Problem
type: Technical Report
number: TR-05-20
institution: University of Pisa
address: Pisa, Italy
year: 2005
bibdate: Wed Nov 30 08:06:39 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.11.30 08:06:39 ???
acknowledgement: ack-nhfb
rawauthor: Gianna M. {Del Corso} and Antonio Gull{\'\i} and Francesco Romani
rawtitle: Comparison of {Krylov} Subspace Methods on the {PageRank} Problem
entry:
@TechReport{DelCorso:2005:CKS,
  author =       "Gianna M. {Del Corso} and Antonio Gull{\'\i} and
                 Francesco Romani",
  title =        "Comparison of {Krylov} Subspace Methods on the
                 {PageRank} Problem",
  type =         "Technical Report",
  number =       "TR-05-20",
  institution =  "University of Pisa",
  address =      "Pisa, Italy",
  pages =        "????",
  year =         "2005",
  bibdate =      "Wed Nov 30 08:06:39 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: DelCorso:2005:FPC
author: Gianna M. Del Corso and Antonio Gulli and Francesco Romani
title: Fast PageRank computation via a sparse linear system
journal: j-INTERNET-MATH
volume: 2
number: 3
pages: 251--273
year: 2005
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68U35 (05C80 65F50)
MRnumber: MR2212366 (2006j:68131)
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1150474883
fjournal: Internet Mathematics
ZMnumber: 1095.68578
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Gianna M. {Del Corso} and Antonio Gull{\'\i} and Francesco Romani
rawtitle: Fast {PageRank} computation via a sparse linear system
entry:
@Article{DelCorso:2005:FPC,
  author =       "Gianna M. {Del Corso} and Antonio Gull{\'\i} and
                 Francesco Romani",
  title =        "Fast {PageRank} computation via a sparse linear
                 system",
  journal =      j-INTERNET-MATH,
  volume =       "2",
  number =       "3",
  pages =        "251--273",
  year =         "2005",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C80 65F50)",
  MRnumber =     "MR2212366 (2006j:68131)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1150474883",
  ZMnumber =     "1095.68578",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: article
filename: pagerank.bib
label: Dominich:2005:PII
author: Sandor Dominich and Adrienn Skrop
title: PageRank and Interaction Information Retrieval: Research Articles
journal: Journal of the American Society for Information Science and Technology
volume: 56
number: 1
pages: 63--69
month: jan
monthnumber: 1
year: 2005
CODEN: JASIEF
DOI: https://doi.org/10.1002/asi.v56:1
ISSN: 1532-2882 (print), 1532-2890 (electronic) OR 15322882 (print), 15322890 (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
fjournal: Journal of the American Society for Information Science and Technology
acknowledgement: ack-nhfb
ajournal: J. Am. Soc. Inf. Sci. Technol.
rawauthor: S{\'a}ndor Dominich and Adrienn Skrop
rawtitle: {PageRank} and Interaction Information Retrieval: Research Articles
abstract:
The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (I 2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based on dynamic systems. In the present paper, a different interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.

rawabstract:
The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (I 2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based on dynamic systems. In the present paper, a different interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.

entry:
@Article{Dominich:2005:PII,
  author =       "S{\'a}ndor Dominich and Adrienn Skrop",
  title =        "{PageRank} and Interaction Information Retrieval:
                 Research Articles",
  journal =      "Journal of the American Society for Information
                 Science and Technology",
  volume =       "56",
  number =       "1",
  pages =        "63--69",
  month =        jan,
  year =         "2005",
  CODEN =        "JASIEF",
  DOI =          "https://doi.org/10.1002/asi.v56:1",
  ISSN =         "1532-2882 (print), 1532-2890 (electronic)",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The PageRank method is used by the Google Web search
                 engine to compute the importance of Web pages. Two
                 different views have been developed for the
                 interpretation of the PageRank method and values: (a)
                 stochastic (random surfer): the PageRank values can be
                 conceived as the steady-state distribution of a Markov
                 chain, and (b) algebraic: the PageRank values form the
                 eigenvector corresponding to eigenvalue 1 of the Web
                 link matrix. The Interaction Information Retrieval (I 2
                 R) method is a nonclassical information retrieval
                 paradigm, which represents a connectionist approach
                 based on dynamic systems. In the present paper, a
                 different interpretation of PageRank is proposed,
                 namely, a dynamic systems viewpoint, by showing that
                 the PageRank method can be formally interpreted as a
                 particular case of the Interaction Information
                 Retrieval method; and thus, the PageRank values may be
                 interpreted as neutral equilibrium points of the Web.",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Am. Soc. Inf. Sci. Technol.",
  fjournal =     "Journal of the American Society for Information
                 Science and Technology",
}


bibtype: inproceedings
filename: pagerank.bib
label: Eirinaki:2005:UBP
author: Magdalini Eirinaki and Michalis Vazirgiannis
title: Usage-based PageRank for Web personalization
crossref: Han:2005:FII
pages: 130--137
year: 2005
DOI: https://doi.org/10.1109/ICDM.2005.148
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1565671
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10470
rawauthor: Magdalini Eirinaki and Michalis Vazirgiannis
rawtitle: Usage-based {PageRank} for {Web} personalization
abstract:
Recommendation algorithms aim at proposing 'next' pages to a user based on her current visit and the past users' navigational patterns. In the vast majority of related algorithms, only the usage data are used to produce recommendations, whereas the structural properties of the Web graph are ignored. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this paper we present UPR, a novel personalization algorithm which combines usage data and link analysis techniques for ranking and recommending web pages to the end user. Using the web site's structure and its usage data we produce personalized navigational graph synopses (prNG) to be used for applying UPR and produce personalized recommendations. Experimental results show that the accuracy of the recommendations is superior to pure usage-based approaches.

rawabstract:
Recommendation algorithms aim at proposing 'next' pages to a user based on her current visit and the past users' navigational patterns. In the vast majority of related algorithms, only the usage data are used to produce recommendations, whereas the structural properties of the Web graph are ignored. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this paper we present UPR, a novel personalization algorithm which combines usage data and link analysis techniques for ranking and recommending web pages to the end user. Using the web site's structure and its usage data we produce personalized navigational graph synopses (prNG) to be used for applying UPR and produce personalized recommendations. Experimental results show that the accuracy of the recommendations is superior to pure usage-based approaches.

entry:
@InProceedings{Eirinaki:2005:UBP,
  author =       "Magdalini Eirinaki and Michalis Vazirgiannis",
  title =        "Usage-based {PageRank} for {Web} personalization",
  crossref =     "Han:2005:FII",
  pages =        "130--137",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/ICDM.2005.148",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1565671",
  abstract =     "Recommendation algorithms aim at proposing 'next'
                 pages to a user based on her current visit and the past
                 users' navigational patterns. In the vast majority of
                 related algorithms, only the usage data are used to
                 produce recommendations, whereas the structural
                 properties of the Web graph are ignored. We claim that
                 taking also into account the web structure and using
                 link analysis algorithms ameliorates the quality of
                 recommendations. In this paper we present UPR, a novel
                 personalization algorithm which combines usage data and
                 link analysis techniques for ranking and recommending
                 web pages to the end user. Using the web site's
                 structure and its usage data we produce personalized
                 navigational graph synopses (prNG) to be used for
                 applying UPR and produce personalized recommendations.
                 Experimental results show that the accuracy of the
                 recommendations is superior to pure usage-based
                 approaches.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10470",
}


bibtype: article
filename: pagerank.bib
label: Farahat:2005:ARH
author: Ayman Farahat and Thomas LoFaro and Joel C. Miller and Gregory Rae and Lesley A. Ward
journal: j-SIAM-J-SCI-COMP
volume: 27
number: 4
pages: 1181--1201
month: jul
monthnumber: 7
year: 2005
CODEN: SJOCE3
DOI: https://doi.org/10.1137/S1064827502412875
ISSN: 1064-8275 (print), 1095-7197 (electronic) OR 10648275 (print), 10957197 (electronic)
ISSNL: 1064-8275 OR 10648275
MRclass: 68U35 (15A18 15A48 68R10 68W40)
MRnumber: MR2199745 (2006m:68169)
MRreviewer: Mirel Co{\c{s}}ulschi
bibdate: Tue Jun 27 09:24:24 MDT 2006
bibsource: http://epubs.siam.org/sam-bin/dbq/toc/SISC/27/4; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2006.06.27 09:24:24 MDT
URL: http://epubs.siam.org/volume-27/art_41287.html
fjournal: SIAM Journal on Scientific Computing
ZMnumber: 1094.68111
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sisc
rawauthor: Ayman Farahat and Thomas LoFaro and Joel C. Miller and Gregory Rae and Lesley A. Ward
title:
Authority Rankings from HITS, PageRank, and SALSA: Existence, Uniqueness, and Effect of Initialization

abstract:
Algorithms such as Kleinberg's HITS algorithm, the PageRank algorithm of Brin and Page, and the SALSA algorithm of Lempel and Moran use the link structure of a network of web pages to assign weights to each page in the network. The weights can then be used to rank the pages as authoritative sources. These algorithms share a common underpinning; they find a dominant eigenvector of a nonnegative matrix that describes the link structure of the given network and use the entries of this eigenvector as the page weights. We use this commonality to give a unified treatment, proving the existence of the required eigenvector for the PageRank, HITS, and SALSA algorithms, the uniqueness of the PageRank eigenvector, and the convergence of the algorithms to these eigenvectors. However, we show that the HITS and SALSA eigenvectors need not be unique. We examine how the initialization of the algorithms affects the final weightings produced. We give examples of networks that lead the HITS and SALSA algorithms to return nonunique or nonintuitive rankings. We characterize all such networks in terms of the connectivity of the related HITS authority graph. We propose a modification, Exponentiated Input to HITS, to the adjacency matrix input to the HITS algorithm. We prove that Exponentiated Input to HITS returns a unique ranking, provided that the network is weakly connected. Our examples also show that SALSA can give inconsistent hub and authority weights, due to nonuniqueness. We also mention a small modification to the SALSA initialization which makes the hub and authority weights consistent.

rawabstract:
Algorithms such as Kleinberg's HITS algorithm, the PageRank algorithm of Brin and Page, and the SALSA algorithm of Lempel and Moran use the link structure of a network of web pages to assign weights to each page in the network. The weights can then be used to rank the pages as authoritative sources. These algorithms share a common underpinning; they find a dominant eigenvector of a nonnegative matrix that describes the link structure of the given network and use the entries of this eigenvector as the page weights. We use this commonality to give a unified treatment, proving the existence of the required eigenvector for the PageRank, HITS, and SALSA algorithms, the uniqueness of the PageRank eigenvector, and the convergence of the algorithms to these eigenvectors. However, we show that the HITS and SALSA eigenvectors need not be unique. We examine how the initialization of the algorithms affects the final weightings produced. We give examples of networks that lead the HITS and SALSA algorithms to return nonunique or nonintuitive rankings. We characterize all such networks in terms of the connectivity of the related HITS authority graph. We propose a modification, Exponentiated Input to HITS, to the adjacency matrix input to the HITS algorithm. We prove that Exponentiated Input to HITS returns a unique ranking, provided that the network is weakly connected. Our examples also show that SALSA can give inconsistent hub and authority weights, due to nonuniqueness. We also mention a small modification to the SALSA initialization which makes the hub and authority weights consistent.

rawtitle:
Authority Rankings from {HITS}, {PageRank}, and {SALSA}: Existence, Uniqueness, and Effect of Initialization

entry:
@Article{Farahat:2005:ARH,
  author =       "Ayman Farahat and Thomas LoFaro and Joel C. Miller and
                 Gregory Rae and Lesley A. Ward",
  title =        "Authority Rankings from {HITS}, {PageRank}, and
                 {SALSA}: Existence, Uniqueness, and Effect of
                 Initialization",
  journal =      j-SIAM-J-SCI-COMP,
  volume =       "27",
  number =       "4",
  pages =        "1181--1201",
  month =        jul,
  year =         "2005",
  CODEN =        "SJOCE3",
  DOI =          "https://doi.org/10.1137/S1064827502412875",
  ISSN =         "1064-8275 (print), 1095-7197 (electronic)",
  ISSN-L =       "1064-8275",
  MRclass =      "68U35 (15A18 15A48 68R10 68W40)",
  MRnumber =     "MR2199745 (2006m:68169)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Tue Jun 27 09:24:24 MDT 2006",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SISC/27/4;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://epubs.siam.org/volume-27/art_41287.html",
  ZMnumber =     "1094.68111",
  abstract =     "Algorithms such as Kleinberg's HITS algorithm, the
                 PageRank algorithm of Brin and Page, and the SALSA
                 algorithm of Lempel and Moran use the link structure of
                 a network of web pages to assign weights to each page
                 in the network. The weights can then be used to rank
                 the pages as authoritative sources. These algorithms
                 share a common underpinning; they find a dominant
                 eigenvector of a nonnegative matrix that describes the
                 link structure of the given network and use the entries
                 of this eigenvector as the page weights. We use this
                 commonality to give a unified treatment, proving the
                 existence of the required eigenvector for the PageRank,
                 HITS, and SALSA algorithms, the uniqueness of the
                 PageRank eigenvector, and the convergence of the
                 algorithms to these eigenvectors. However, we show that
                 the HITS and SALSA eigenvectors need not be unique. We
                 examine how the initialization of the algorithms
                 affects the final weightings produced. We give examples
                 of networks that lead the HITS and SALSA algorithms to
                 return nonunique or nonintuitive rankings. We
                 characterize all such networks in terms of the
                 connectivity of the related HITS authority graph. We
                 propose a modification, Exponentiated Input to HITS, to
                 the adjacency matrix input to the HITS algorithm. We
                 prove that Exponentiated Input to HITS returns a unique
                 ranking, provided that the network is weakly connected.
                 Our examples also show that SALSA can give inconsistent
                 hub and authority weights, due to nonuniqueness. We
                 also mention a small modification to the SALSA
                 initialization which makes the hub and authority
                 weights consistent.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Scientific Computing",
  journal-URL =  "http://epubs.siam.org/sisc",
}


bibtype: article
filename: pagerank.bib
label: Fogaras:2005:TSF
author: Daniel Fogaras and Balazs Racz and Karoly Csalogany and Tamas Sarlos
title: Towards scaling fully personalized PageRank: algorithms, lower bounds, and experiments
journal: j-INTERNET-MATH
volume: 2
number: 3
pages: 333--358
year: 2005
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68U35 (05C80 68R10)
MRnumber: MR2212369 (2006j:68132)
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1150474886
fjournal: Internet Mathematics
ZMnumber: 1095.68579
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: D{\'a}niel Fogaras and Bal{\'a}zs R{\'a}cz and K{\'a}roly Csalog{\'a}ny and Tam{\'a}s Sarl{\'o}s
rawtitle: Towards scaling fully personalized {PageRank}: algorithms, lower bounds, and experiments
entry:
@Article{Fogaras:2005:TSF,
  author =       "D{\'a}niel Fogaras and Bal{\'a}zs R{\'a}cz and
                 K{\'a}roly Csalog{\'a}ny and Tam{\'a}s Sarl{\'o}s",
  title =        "Towards scaling fully personalized {PageRank}:
                 algorithms, lower bounds, and experiments",
  journal =      j-INTERNET-MATH,
  volume =       "2",
  number =       "3",
  pages =        "333--358",
  year =         "2005",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C80 68R10)",
  MRnumber =     "MR2212369 (2006j:68132)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1150474886",
  ZMnumber =     "1095.68579",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: article
filename: pagerank.bib
label: Gori:2005:EAG
author: M. Gori and M. Maggini and L. Sarti
title: Exact and approximate graph matching using random walks
journal: j-IEEE-TRANS-PATT-ANAL-MACH-INTEL
volume: 27
number: 7
pages: 1100--1111
month: jul
monthnumber: 7
year: 2005
CODEN: ITPIDJ
DOI: https://doi.org/10.1109/TPAMI.2005.138
ISSN: 0162-8828 OR 01628828
ISSNL: 0162-8828 OR 01628828
bibdate: Thu May 06 14:59:25 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 14:59:25 ???
fjournal: IEEE Transactions on Pattern Analysis and Machine Intelligence
acknowledgement: ack-nhfb
journalURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34
rawauthor: M. Gori and M. Maggini and L. Sarti
rawtitle: Exact and approximate graph matching using random walks
entry:
@Article{Gori:2005:EAG,
  author =       "M. Gori and M. Maggini and L. Sarti",
  title =        "Exact and approximate graph matching using random
                 walks",
  journal =      j-IEEE-TRANS-PATT-ANAL-MACH-INTEL,
  volume =       "27",
  number =       "7",
  pages =        "1100--1111",
  month =        jul,
  year =         "2005",
  CODEN =        "ITPIDJ",
  DOI =          "https://doi.org/10.1109/TPAMI.2005.138",
  ISSN =         "0162-8828",
  ISSN-L =       "0162-8828",
  bibdate =      "Thu May 06 14:59:25 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE Transactions on Pattern Analysis and Machine
                 Intelligence",
  journal-URL =  "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34",
}


bibtype: article
filename: pagerank.bib
label: Higham:2005:GPM
author: Desmond J. Higham
title: Google PageRank as mean playing time for pinball on the reverse web
journal: j-APPL-MATH-LETT
volume: 18
number: 12
pages: 1359--1362
year: 2005
CODEN: AMLEEL
DOI: https://doi.org/10.1016/j.aml.2005.02.020
ISSN: 0893-9659 (print), 1873-5452 (electronic) OR 08939659 (print), 18735452 (electronic)
ISSNL: 0893-9659 OR 08939659
MRclass: 68U35 (60J10 60J20)
MRnumber: MR2189889
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
fjournal: Applied Mathematics Letters. An International Journal of Rapid Publication
ZMnumber: 1083.68509
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/08939659
rawauthor: Desmond J. Higham
rawtitle: {Google PageRank} as mean playing time for pinball on the reverse web
entry:
@Article{Higham:2005:GPM,
  author =       "Desmond J. Higham",
  title =        "{Google PageRank} as mean playing time for pinball on
                 the reverse web",
  journal =      j-APPL-MATH-LETT,
  volume =       "18",
  number =       "12",
  pages =        "1359--1362",
  year =         "2005",
  CODEN =        "AMLEEL",
  DOI =          "https://doi.org/10.1016/j.aml.2005.02.020",
  ISSN =         "0893-9659 (print), 1873-5452 (electronic)",
  ISSN-L =       "0893-9659",
  MRclass =      "68U35 (60J10 60J20)",
  MRnumber =     "MR2189889",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1083.68509",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics Letters. An International Journal
                 of Rapid Publication",
  journal-URL =  "http://www.sciencedirect.com/science/journal/08939659",
}


bibtype: article
filename: pagerank.bib
label: Ipsen:2005:CAP
author: Ilse C. F. Ipsen and Steve Kirkland
title: Convergence Analysis of a PageRank Updating Algorithm by Langville and Meyer
journal: j-SIAM-J-MAT-ANA-APPL
volume: 27
number: 4
pages: 952--967
year: 2005
CODEN: SJMAEL
DOI: https://doi.org/10.1137/S0895479804439808
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
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: SIAM Journal on Matrix Analysis and Applications
keywords: aggregation/disaggregation; Google; Markov chain; PageRank; power method; stochastic complement
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Ilse C. F. Ipsen and Steve Kirkland
rawtitle: Convergence Analysis of a {PageRank} Updating Algorithm by {Langville} and {Meyer}
abstract:
The PageRank updating algorithm proposed by Langville and Meyer is a special case of an iterative aggregation/disaggregation (SIAD) method for computing stationary distributions of very large Markov chains. It is designed, in particular, to speed up the determination of PageRank, which is used by the search engine Google in the ranking of web pages. In this paper the convergence, in exact arithmetic, of the SIAD method is analyzed. The SIAD method is expressed as the power method preconditioned by a partial LU factorization. This leads to a simple derivation of the asymptotic convergence rate of the SIAD method. It is known that the power method applied to the Google matrix always converges, and we show that the asymptotic convergence rate of the SIAD method is at least as good as that of the power method. Furthermore, by exploiting the hyperlink structure of the web it can be shown that the asymptotic convergence rate of the SIAD method applied to the Google matrix can be made strictly faster than that of the power method.

rawabstract:
The PageRank updating algorithm proposed by Langville and Meyer is a special case of an iterative aggregation/disaggregation (SIAD) method for computing stationary distributions of very large Markov chains. It is designed, in particular, to speed up the determination of PageRank, which is used by the search engine Google in the ranking of web pages. In this paper the convergence, in exact arithmetic, of the SIAD method is analyzed. The SIAD method is expressed as the power method preconditioned by a partial LU factorization. This leads to a simple derivation of the asymptotic convergence rate of the SIAD method. It is known that the power method applied to the Google matrix always converges, and we show that the asymptotic convergence rate of the SIAD method is at least as good as that of the power method. Furthermore, by exploiting the hyperlink structure of the web it can be shown that the asymptotic convergence rate of the SIAD method applied to the Google matrix can be made strictly faster than that of the power method.

entry:
@Article{Ipsen:2005:CAP,
  author =       "Ilse C. F. Ipsen and Steve Kirkland",
  title =        "Convergence Analysis of a {PageRank} Updating
                 Algorithm by {Langville} and {Meyer}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "27",
  number =       "4",
  pages =        "952--967",
  year =         "2005",
  CODEN =        "SJMAEL",
  DOI =          "https://doi.org/10.1137/S0895479804439808",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The PageRank updating algorithm proposed by Langville
                 and Meyer is a special case of an iterative
                 aggregation/disaggregation (SIAD) method for computing
                 stationary distributions of very large Markov chains.
                 It is designed, in particular, to speed up the
                 determination of PageRank, which is used by the search
                 engine Google in the ranking of web pages. In this
                 paper the convergence, in exact arithmetic, of the SIAD
                 method is analyzed. The SIAD method is expressed as the
                 power method preconditioned by a partial LU
                 factorization. This leads to a simple derivation of the
                 asymptotic convergence rate of the SIAD method. It is
                 known that the power method applied to the Google
                 matrix always converges, and we show that the
                 asymptotic convergence rate of the SIAD method is at
                 least as good as that of the power method. Furthermore,
                 by exploiting the hyperlink structure of the web it can
                 be shown that the asymptotic convergence rate of the
                 SIAD method applied to the Google matrix can be made
                 strictly faster than that of the power method.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
  keywords =     "aggregation/disaggregation; Google; Markov chain;
                 PageRank; power method; stochastic complement",
}


