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Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model

by Prerna Rai, Arvind Lal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 9
Year of Publication: 2016
Authors: Prerna Rai, Arvind Lal
10.5120/ijca2016908942

Prerna Rai, Arvind Lal . Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model. International Journal of Computer Applications. 138, 9 ( March 2016), 9-13. DOI=10.5120/ijca2016908942

@article{ 10.5120/ijca2016908942,
author = { Prerna Rai, Arvind Lal },
title = { Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 9 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number9/24405-2016908942/ },
doi = { 10.5120/ijca2016908942 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:39:13.299464+05:30
%A Prerna Rai
%A Arvind Lal
%T Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 9
%P 9-13
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this document, the algorithm behind Google PageRanking and their techniques have been put up. The basic algorithm used by Google, for PageRanking and other applications are Markov model or Markov Chain model and Hidden Markov model. These algorithms are used to search and rank websites in the Google search engine. PageRank is a way of measuring the importance of website pages. Markov chain model and Hidden Markov model is a mathematical system model. It describes transitions from one state to another in a state space. The Markov model is based on the probability the user will select the page and based on the number of incoming and outgoing links, ranks for the pages are determined. HMM also finds its application within Mapper/Reducer. These algorithms are a link analysis algorithm.

References
  1. http://en.wikipedia.org/wiki/PageRank
  2. Page Ranking Based on Number of Visits of Links of Web Page Gyanendra Kumar1, Neelam Duhan2, A. K. Sharma3 IEEE, International Conference on Computer & Communication Technology (ICCCT)-2011
  3. The Application of Hidden Markov Models in Speech Recognition, Mark Gales1 and Steve Young2, Foundations and TrendsR_ in Signal Processing Vol. 1, No. 3 (2007) 195–304_c 2008 M. Gales and S. Young.
  4. Hidden Markov Models and other Finite State Automata for Sequence Processing Herv´e Bourlardy;z and Samy Bengioy y Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAP)/handbook.
  5. Hidden Markov models David M. Blei March 12, 2012Finite State Machines, inst.eecs.berkeley.edu /~cs61c /sp08/labs/10/PH-B10.pdf
  6. Markov Chain Interpretation of Google Page RankJia Li December 1, 2005.
  7. Speech Recognition. B. Paul .Speech Recognition Using. Hidden Makov Models. https://www.ll.mit.edu /publications/journal/3.1.3.pdf
  8. A Look at Markov Chains and their Use in Google Rebecca Atherton Iowa State University MSM Creative Component Summer 2005
  9. The Performance of Page Rank Algorithm underDegree Preserving Perturbations (Senanayake, Peter Szot, Mahendra Piraveenan, Dharshana Kasthurirathna)University of Sydney, NSW 2006, Australia.
  10. Using a Layered Markov Model for Distributed Web Ranking Computation. J Wu, K Aberer ICDCS 2005. Proceedings. 25th IEEE, 2005.
  11. Reduced-Rank Hidden Markov ModelsAn Introduction to Hidden Markov Models, Max Heimel 07.10.2010.
  12. PageRank Ryan Tibshirani Data Mining: 36—462/36~662 January 22 2013.
  13. An Introduction to Hidden Markov Modelsisabel-drost.de/hadoop/slides/HMM.pdf
  14. Application of Markov Chain in the page rank algorithm.Ravi kumar, Alex GOh Kwang Leng,Ashutosh kumar Singh.
  15. Stochastic finite state machine for Markov Model and HMM.pg no 108-112
Index Terms

Computer Science
Information Sciences

Keywords

Markov chain Model PageRanking Finite state machine.

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