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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.
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Glen Jeh
Google (United States)
Jennifer Widom
Microsoft (United States)
Stanford University
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Jeh et al. (Tue,) studied this question.
synapsesocial.com/papers/6a0eadffb7cc3b883f229f22 — DOI: https://doi.org/10.1145/775047.775126