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2005
ACM

Scaling link-based similarity search

9 years 9 months ago
Scaling link-based similarity search
To exploit the similarity information hidden in the hyperlink structure of the web, this paper introduces algorithms scalable to graphs with billions of vertices on a distributed architecture. The similarity of multi-step neighborhoods of vertices are numerically evaluated by similarity functions including SimRank [20], a recursive refinement of cocitation; PSimRank, a novel variant with better theoretical characteristics; and the Jaccard coefficient, extended to multi-step neighborhoods. Our methods are presented in a general framework of Monte Carlo similarity search algorithms that precompute an index database of random fingerprints, and at query time, similarities are estimated from the fingerprints. The performance and quality of the methods were tested on the Stanford Webbase [19] graph of 80M pages by comparing our scores to similarities extracted from the ODP directory [26]. Our experimental results suggest that the hyperlink structure of vertices within four to five steps pro...
Balázs Rácz, Dániel Fogaras
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2005
Where WWW
Authors Balázs Rácz, Dániel Fogaras
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