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

Kurt Bryan and Tanya Leise
SIAM Review
Vol. 48, No. 3 (Sep., 2006), pp. 569-581
Stable URL: http://www.jstor.org/stable/20453840
Page Count: 13
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The $25,000,000,000 Eigenvector: The Linear Algebra behind Google
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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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