Taming computational complexity: efficient and parallel simrank optimizations on undirected graphs

  • Authors:

  • Weiren Yu;Xuemin Lin;Jiajin Le

  • Affiliations:

  • Donghua University, Shanghai, China and University of New South Wales, NSW, Australia;University of New South Wales, NSW, Australia;Donghua University, Shanghai, China

  • Venue:
  • WAIM'10 Proceedings of the 11th international conference on Web-age information management
  • Year:
  • 2010

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Abstract

SimRank has been considered as one of the promising link-based ranking algorithms to evaluate similarities of web documents in many modern search engines. In this paper, we investigate the optimization problem of Sim-Rank similarity computation on undirected web graphs. We first present a novel algorithm to estimate the SimRank between vertices in O(n3 + K ċ n2) time, where n is the number of vertices, and K is the number of iterations. In comparison, the most efficient implementation of SimRank algorithm in [1] takes O(K ċ n3) time in the worst case. To efficiently handle large-scale computations, we also propose a parallel implementation of the SimRank algorithm on multiple processors. The experimental evaluations on both synthetic and real-life data sets demonstrate the better computational time and parallel efficiency of our proposed techniques.