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Searching the World Wide Web is an NP complete problem with sparse hyperlink matrices. Thus searching the significant search results is a challenge. Google’s PageRank attempted to solve this problem using computing of principle Eigenvalues termed as PageRank vector. After this, a number of techniques were developed to speed up the convergence patterns of pages in the PageRank algorithm. This is paper, we have reviewed a number of of PageRank computation techniques. The main objective of all these techniques is the convergence rate along with space and time complexities. In this paper, a comparative study is presented among Standard Power method, Adaptive Power Method and Aitken’s method using SNAP Google web pages dataset.

A Survey and Comparative Study of Different PageRank Algorithms