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.
Kurt Bryan and Tanya Leise (200REF) had analyzed the page rank formula that ranks the importance of web pages according to an eigen vector of a weighted link matrix in their work REF .
The $25,000,000,000 eigenvector: the linear algebra behind Google
SIAM Review
Computer Science Mathematics