Wolfgang Schoch

THE $25,000,000,000 EIGENVECTOR - Lineare Mathematik hinter Google’s Pagerank

Ein interessantes Paper, dass den Google PageRank mit Hilfe von linearer Algebra erläutert.

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.

Das Paper sowie weiteres Material gibts auf der Webseite von Autor Prof. Kurt Bryan und ist nicht nur für Informatiker oder Mathematiker verständlich.