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An Improved Method for Efficient PageRank Estimation

  • Yuta Sakakura
  • Yuto Yamaguchi
  • Toshiyuki Amagasa
  • Hiroyuki Kitagawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8645)

Abstract

PageRank is a link analysis method to estimate the importance of nodes in a graph, and has been successfully applied in wide range of applications. However, its computational complexity is known to be high. Besides, in many applications, only a small number of nodes are of interest. To address this problem, several methods for estimating PageRank score of a target node without accessing whole graph have been proposed. In particular, Chen et al. proposed an approach where, given a target node, subgraph containing the target is induced to locally compute PageRank score. Nevertheless, its computation is still time consuming due to the fact that a number of iterative processes are required when constructing a subgraph for subsequent PageRank estimation. To make it more efficient, we propose an improved approach in which a subgraph is recursively expanded by solving a linear system without any iterative computation. To assess the efficiency of the proposed scheme, we conduct a set of experimental evaluations. The results reveal that our proposed scheme can estimate PageRank score more efficiently than the existing approach while maintaining the estimation accuracy.

Keywords

PageRank Local Estimation Link Structure Analysis 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yuta Sakakura
    • 1
  • Yuto Yamaguchi
    • 1
  • Toshiyuki Amagasa
    • 2
  • Hiroyuki Kitagawa
    • 2
  1. 1.Graduate School of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  2. 2.Faculty of Engineering, Information and SystemsUniversity of TsukubaTsukubaJapan

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