In this document, the Google Page ranking and the algorithms behind this technique have been put up. Google, in its efforts to crawl and index the Web efficiently and produce much more satisfying search results than other existing systems, has been continuously working on optimizing its search results. This optimization is done by a mechanism called PageRank. Page ranking for Google has been implemented using Markov chain and we present here the use of Hidden Markov Model (HMM) in Google page ranking and its implications. 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 are mathematical system models. They describe 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 link analysis algorithms. The current paper presents an analytical study on the application of HMM in Google Page Ranking.

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Damping Factor Links Markov Model Pagerank Probability Web-graph Stochastic