Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A fast two-stage algorithm for computing SimRank and its extensions
WAIM'10 Proceedings of the 2010 international conference on Web-age information management
Pairwise similarity calculation of information networks
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Scalable and axiomatic ranking of network role similarity
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
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Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiting the object-to-object relationship. Unfortunately, it is prohibitively expensive to compute the link-based similarity in a relatively large graph. In this paper, based on the observation that link-based similarity scores of real world graphs follow the power-law distribution, we propose a new approximate algorithm, namely Power-SimRank, with guaranteed error bound to efficiently compute link-based similarity measure. We also prove the convergence of the proposed algorithm. Extensive experiments conducted on real world datasets and synthetic datasets show that the proposed algorithm outperforms SimRank by four-five times in terms of efficiency while the error generated by the approximation is small.