Probesim

Single-source and top-kSimRank queries are two important types of similarity search in graphs with numerous applications in web mining, social network analysis, spam detection, etc. A plethora of techniques have been proposed for these two types of queries, but very few can efficiently support similarity search over large dynamic graphs, due to either significant preprocessing time or large space overheads.This paper presents ProbeSim, an index-freealgorithm for single-source and top-kSimRank queries that provides a non-trivial theoretical guarantee in the absolute error of query results. ProbeSimestimates SimRank similarities without precomputing any indexing structures, and thus can naturally support real-timeSimRank queries on dynamicgraphs. Besides the theoretical guarantee, ProbeSimalso offers satisfying practical efficiency and effectiveness due to non-trivial optimizations. We conduct extensive experiments on a number of benchmark datasets, which demonstrate that our solutions outperform the existing methods in terms of efficiency and effectiveness. Notably, our experiments include the first empirical study that evaluates the effectiveness of SimRank algorithms on graphs with billion edges, using the idea of pooling.