Abstract
Brin and Larry proposed PageRank in 1998, which appears as a prevailing link analysis technique used by web search engines to rank its search results list. Computation of PageRank values in an efficient and faster manner for very immense web graph is truly an essential concern for search engines today. To identify the spam web pages and also deal with them is yet another important concern in web browsing. In this research article, an efficient and faster parallel PageRank algorithm is proposed, which harnesses the power of graphics processing units (GPUs). In proposed algorithm, the PageRank scores are non-uniformly distributes among the web pages, so it is also competent of coping with spam web pages. The experiments are performed on standard datasets available in Stanford large network dataset collection. There is a speed up of about 1.1 to 1.7 for proposed parallel PageRank algorithm over existing parallel PageRank algorithm.
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