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Reseach Article

Random Web Surfer PageRank Algorithm

by Navadiya Hareshkumar, Dr. Deepak Garg
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 11
Year of Publication: 2011
Authors: Navadiya Hareshkumar, Dr. Deepak Garg
10.5120/4448-6214

Navadiya Hareshkumar, Dr. Deepak Garg . Random Web Surfer PageRank Algorithm. International Journal of Computer Applications. 35, 11 ( December 2011), 36-41. DOI=10.5120/4448-6214

@article{ 10.5120/4448-6214,
author = { Navadiya Hareshkumar, Dr. Deepak Garg },
title = { Random Web Surfer PageRank Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 11 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number11/4448-6214/ },
doi = { 10.5120/4448-6214 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:21:43.919907+05:30
%A Navadiya Hareshkumar
%A Dr. Deepak Garg
%T Random Web Surfer PageRank Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 11
%P 36-41
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
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.

References
  1. Desmond J. Higham and Alan Taylor, The Sleekest Link Algorithm (2003).
  2. Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd, The PageRank Citation Ranking: Bringing Order to the Web (1998).
  3. Amy N. Langville and Carl D. Meyer, The Use of the Linear Algebra by Web Search Engines (2004).
  4. Eric W. Weisstein, Adjacency Matrix, From MathWorld- A Wolfram Web Resource.
  5. Amy N. Langville, Carl D. Meyer, Deeper InsidePageRank(2004).
  6. Eric W. Weisstein, Markov Chain, From MathWorld– A Wolfram Web Resource.
  7. David Nelson, editor, The Penguin Dictionary of Mathematics (Penguin Books Ltd, London, 2003).
  8. Sergio S. Guirreri, Markov Chains as methodology used byPageRank to rank the Web Pages on Internet (2010).
  9. Bill Coughran, Google’s index nearly doubles, Google Inc.(2004)
  10. Kristen Thorson. Modeling the Web and the computation of PageRank (Hollins University, 2004).
  11. Ilse C.F. Ipsen, Steve Kirkland, Convergence Analysis Of An Improved PageRank Algorithm (2003)
  12. Alexander Nazin, Boris Polyak, Adaptive Randomized Algorithm for Finding Eigenvector of Stochastic Matrix with Application to PageRank (48th IEEE Conference- December 16-18, 2009)
  13. MandarKale, Mrs.P.SanthiThilagam, DYNA-RANK: Efficient calculation and updation of PageRank(International Conference on Computer Science and Information Technology 2008)
  14. Sriram Raghavan, Hector Garcia-Molina. Compressing the graph structure of the Web. In Proceedings of the IEEE Conference on Data Compression, pages 213–222, (March 2001).
  15. Cleve B. Moler. Numerical Computing with MATLAB. (SIAM, 2004).
  16. Taher H. Haveliwala. Efficient encodings for documentranking vectors. (Technical report, CS Department,Stanford University, November 2002).
Index Terms

Computer Science
Information Sciences

Keywords

PageRank Markov chains Power method Google matrix Stationary distribution vector Eigen Vector Values