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A heuristic relaxed extrapolated algorithm for accelerating PageRank
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AI
DOI：10.1016/j.advengsoft.2016.01.024.png)
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The PageRank algorithm for determining the importance of Web pages has become a central technique in Web search. This algorithm uses the Power method to compute successive iterates that converge to the principal eigenvector of the Markov chain representing the Web link graph. In this work we present an effective heuristic Relaxed and Extrapolated algorithm based on the Power method that accelerates its convergence. A hybrid parallel implementation of this algorithm has been designed by combining various OpenMP threads for each MPI process and several strategies of data distribution among nodes have been analyzed. The results show that the proposed algorithm can significantly speed up the convergence time with respect to the parallel Power algorithm. (C) 2016 Civil-Comp Ltd. and Elsevier Ltd. All rights reserved.
Keyword:
PageRank
Parallel algorithms
Power method
Relaxation and extrapolation
Shared memory
Distributed memory
期刊
IF:
5.7
论文数:
3.3K
被引数:
1.2W
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