A comprehensive structural-based similarity measure in directed graphs
Computing similarity between two nodes in directed graphs plays an increasingly important role in various research fields, including clustering, collaborative filtering and community mining. Many similarity measures have been devoted in recent years, such as SimRank, PSimRank and SimFusion. However, these measures consider only the expected meeting probability of equal path length, which may omit some latent similar nodes. Besides, the link importance of each edge is not distinguished, which may lead to unreasonable rankings while searching similar nodes. In this paper, we propose an effective structural-based similarity measure, ESimRank, for effectively computing similarities in directed graphs. We firstly define effective relationship strength (ERS) to distinguish link importance by utilizing node activity, node attraction and link frequency. And then we formalize ESimRank equation by combining ERS and the expected meeting probabilities of any path length. Compared to existing similarity measures, ESimRank can find more latent similar nodes and give ranking of better quality. For supporting fast similarity computation, we develop an extended partial sums-based algorithm, which reduces the time complexity significantly. Extensive experiments demonstrate the effectiveness and efficiency of ESimRank by comparing with the state-of-the-art similarity measures.
similarity esimrank effective relationship strength