The $25,000,000,000 eigenvector: The linear algebra behind google

被引:189
作者
Bryan, Kurt [1 ]
Leise, Tanya
机构
[1] Rose Hulman Inst Technol, Dept Math, Terre Haute, IN 47803 USA
[2] Amherst Coll, Dept Math & Comp Sci, Amherst, MA 01002 USA
关键词
linear algebra; PageRank; eigenvector; stochastic matrix;
D O I
10.1137/050623280
中图分类号
O29 [应用数学];
学科分类号
070104 [应用数学];
摘要
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-liulman.edu/similar to bryan.
引用
收藏
页码:569 / 581
页数:13
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