The DOI matches its Crossref record; 22 of the 23 references it cites were found in a registry or an index.
Using PageRank to Characterize Web Structure
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
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Cited by
Sorted by citations
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Metadata
- Authors
- Pandurangan, G., Raghavan, P., & Upfal, E.
- Affiliations
- Brown University (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- 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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