Cited by 164Complex Network Analysis TechniquesInformation Retrieval and Search BehaviorWeb visibility and informetrics

Using PageRank to Characterize Web Structure

Pandurangan, Gopal, Raghavan, Prabhakar, Upfal, Eli·2002·Lecture Notes in Computer Science

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Crossref · 10.1007/3-540-45655-4_36

Cites

22 of 22 references verified0 citation errors · 0 not checkable

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  • In the Web, the power-law exponents are believed to satisfy α = 1.1 for the in-degree and PageRank, and α ≈ 2 for the out-degree.

    “In the Web, the power law exponents can deviate depending on a data set and an estimator, but are believed to satisfy α = 1 . 1 for the in-degree and PageRank, and α ≈ 2 for the out-degree [9], [32], [38].”

    From Asymptotic analysis for personalized Web search (2010) · cites this paper as [32]

  • Most experimental studies of the Web agree that the in-degree, the out-degree, and the PageRank of the Web follow power laws.

    “Most experimental studies of the Web agree that the in-degree, the out-degree, and the PageRank of the Web follow power laws.”

    From Asymptotic analysis for personalized Web search (2010) · cites this paper as [32]

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Authors
Pandurangan, G., Raghavan, P., & Upfal, E.
Affiliations
Brown University (US)
Venue
Lecture Notes in Computer Science
Published
2002
Pages
330-339
ISSN
0302-9743 (ISSN-L)
OpenAlex
W2170582301
Cited by
164
References
23
Citations by year
2025: 2 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 2 · 2019: 4 · 2018: 1 · 2017: 3 · 2016: 3 · 2015: 4 · 2014: 8 · 2013: 6 · 2012: 6
FWCI
33.20
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
Keywords
PageRank, web graph, power-law distribution, degree distribution, search engine indexing, graph model, heavy-tailed distributions

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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