E-rank: A Structural-Based Similarity Measure in Social Networks

Abstract

With the social networks (SNs) becoming ubiquitous and massive, the issue of similarity computation among entities becomes more challenging and draws extensive interests from various research fields. SimRank is a well known similarity measure, however it considers only the meetings between two nodes that walk along equal length paths since the path length increases strictly with the iteration increasing during the similarity computation, besides, it does not differentiate importance for each link. In this paper, we propose a novel structural similarity measure, E-Rank (Entity Rank), towards effectively computing the structural similarity of entities in SNs, based on the intuition that two entities are similar if they can arrive at common entities. E-Rank can be well applied to social networks for measuring similarities of entities. Extensive experiments demonstrate the effectiveness of E-Rank by comparing with the state-of-the-art measures.