Google publishes a whole lot of interesting papers regularly at their Research Publications page. I tend to check up now and then since they do quite a bit of research regarding machine learning and artificial intelligence - my great love interests . Anyway, today I came across a simple paper published elsewhere on the use of Eigenvectors in Google's PageRank algorithms. It is an interesting read - would be more so for anyone with a bigger fancy for mathematics and a crush on Google.

The $25,000,000,000 Eigenvector: The linear algebra behind Google

Abstract. Google’s success derives in large part from its PageRank algorithm, which ranks the importance of webpages 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.

Grab the paper at http://www.rose-hulman.edu/~bryan/googleFinalVersionFixed.pdf