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Random Web Surfer PageRank Algorithm

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 35 - Number 11
Year of Publication: 2011
Authors:
Navadiya Hareshkumar
Dr. Deepak Garg
10.5120/4448-6214

Navadiya Hareshkumar and Dr. Deepak Garg. Article: Random Web Surfer PageRank Algorithm. International Journal of Computer Applications 35(11):36-41, December 2011. Full text available. BibTeX

@article{key:article,
	author = {Navadiya Hareshkumar and Dr. Deepak Garg},
	title = {Article: Random Web Surfer PageRank Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {35},
	number = {11},
	pages = {36-41},
	month = {December},
	note = {Full text available}
}

Abstract

In 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.

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