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The $25,000,000,000 Eigenvector: The Linear Algebra behind Google
Kurt Bryan and Tanya Leise
Vol. 48, No. 3 (Sep., 2006), pp. 569-581
Published by: Society for Industrial and Applied Mathematics
Stable URL: http://www.jstor.org/stable/20453840
Page Count: 13
You can always find the topics here!Topics: Eigenvectors, Hyperlinks, Eigenvalues, Matrices, Search engines, Web pages, Linear algebra, Mathematical vectors, Voting, Markov chains
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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.
SIAM Review © 2006 Society for Industrial and Applied Mathematics