SimRank: a measure of structural-context similarity

  • Authors:
  • Glen Jeh;Jennifer Widom

  • Affiliations:
  • Stanford University;Stanford University

  • Venue:
  • Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2002

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Abstract

The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.