Abstract
Graphs can be found in applications like social networks, bibliographic networks, and biological databases. Understanding the relationship, or links , among graph nodes enables applications such as link prediction, recommendation, and spam detection. In this paper, we propose link-based similarity join (LS-join), which extends the similarity join operator to link-based measures. Given two sets of nodes in a graph, the LS-join returns all pairs of nodes that are highly similar to each other, with respect to an e -function. The e -function generalizes common measures like Personalized PageRank (PPR) and SimRank (SR). We study an efficient LS-join algorithm on a large graph. We further improve our solutions for PPR and SR, which involve expensive random-walk operations. We validate our solutions by performing extensive experiments on three real graph datasets.
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