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Cited by 260

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

The $25,000,000,000 Eigenvector: The Linear Algebra behind Google

Bryan, Kurt, Leise, Tanya·2006·SIAM Review

Abstract

Google's success derives in large part from its PageRank algorithm, which ranks the importance of web pages according to an eigenvector of a weighted link matrix. Analysis of the PageRank formula provides a wonderful applied topic for a linear algebra course. Instructors may assign this article as a project to more advanced students or spend one or two lectures presenting the material with assigned homework from the exercises. This material also complements the discussion of Markov chains in matrix algebra. Maple and Mathematica files supporting this material can be found at www.rose-hulman.edu/~bryan.

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Metadata

Authors
Bryan, K., & Leise, T.
Venue
SIAM Review
Published
2006-01
Volume
48
Issue
3
Pages
569-581
ISSN
  • 0036-1445 (ISSN-L)
  • 1095-7200
OpenAlex
W1996934732
Cited by
260
References
11
FWCI
6.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The $25,000,000,000 Eigenvector: The Linear Algebra behind Google. https://app.cochat.ai/papers/10.1137/050623280
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Bryan, K., & Leise, T. (2006). The $25,000,000,000 Eigenvector: The Linear Algebra behind Google. SIAM Review. https://doi.org/10.1137/050623280

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