Random Web Surfer PageRank Algorithm

TL;DR: This paper analyzes how the Google web search engine implements the PageRank algorithm to define prominent status to web pages in a network and focuses on how to relate the eigenvalues and eigenvector of Google matrix to PageRank values to guarantee that there is a single stationary distribution vector to which thePageRank algorithm converges and efficiently compute the Page Rank for large sets of web Pages.

Abstract: this paper analyzes how the Google web search engine implements the PageRank algorithm to define prominent status to web pages in a network. It describes the PageRank algorithm as a Markov process, web page as state of Markov chain, Link structure of web as Transitions probability matrix of Markov chains, the solution to an eigenvector equation and Vector iteration power method. It mainly focus on how to relate the eigenvalues and eigenvector of Google matrix to PageRank values to guarantee that there is a single stationary distribution vector to which the PageRank algorithm converges and efficiently compute the PageRank for large sets of web Pages. Finally, it will demonstrate example of the PageRank algorithm.