IJISA Vol. 9, No. 9, 8 Sep. 2017

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PageRank method, Power method, Web matrix, Markov model, Eigenvalue problem

Web search engines use various ranking methods to determine the order of web pages displayed on the Search Engine Result Page (SERP). PageRank is one of the popular and widely used ranking method. PageRank of any web page can be defined as a fraction of time a random web surfer spends on that web page on average. The PageRank method is a stationary distribution of a stochastic method whose states are web pages of the Web graph. This stochastic method is acquired by combining the hyperlink matrix of the web graph and a trivial uniform process. This combination is needed to make primitive so that stationary distribution is well defined. The combination depends on the value of damping factor α∈[0,1] in the computation of PageRank. The damping factor parameter state that how much time random web surfer follow hyperlink structure than teleporting. The value of α is exceptionally empirical and in current scenario α = 0.85 is considered as suggested by Brin and Page. If we take α =0.8 then we can say that out of total time, 80% of time is taken by the random web surfer to follow the hyperlink structure and 20% time they teleport to new web pages randomly. Today web surfer gets worn out too early on the web because of non-availability of relevant information and they can easily teleport to new web pages rather than following hyperlink structure. So we have to choose some value of damping factor other than 0.85. In this paper, we have given an experimental analysis of PageRank computation for different value of the damping factor. We have observed that for value of α=0.7, PageRank method takes fewer numbers of iterations to converge than α=0.85, and for these values of α the top 25 web pages returned by PageRank method in the SERP are almost same, only some of them exchange their positions. From the experimental results it is observed that value of damping factor α=0.7 takes approximate 25-30% fewer numbers of iterations than α=0.85 to get closely identical web pages in top 25 result pages for personalized web search, selective crawling, intra-web search engine.

Atul Kumar Srivastava, Rakhi Garg, P. K. Mishra, "Discussion on Damping Factor Value in PageRank Computation", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.9, pp.19-28, 2017. DOI:10.5815/ijisa.2017.09.03

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