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Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model
| 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
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
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