Application of Markov Chain in the PageRank Algorithm

Link analysis algorithms for Web search engines determine the importance and relevance of Web pages. Among the link analysis algorithms, PageRank is the state of the art ranking mechanism that is used in Google search engine today. The PageRank algorithm is modeled as the behavior of a randomized Web surfer; this model can be seen as Markov chain to predict the behavior of a system that travels from one state to another state considering only the current condition. However, this model has the dangling node or hanging node problem because these nodes cannot be presented in a Markov chain model. This paper focuses on the application of Markov chain on PageRank algorithm and discussed a few methods to handle the dangling node problem. The Experiment is done running on WEBSPAM-UK2007 to show the rank results of the dangling nodes.