Community Detection in Multi-relational Social Networks

Multi-relational networks are ubiquitous in many fields such as bibliography, twitter, and healthcare. There have been many studies in the literature targeting at discovering communities from social networks. However, most of them have focused on single-relational networks. A hint of methods detected communities from multi-relational networks by converting them to single-relational networks first. Nevertheless, they commonly assumed different relations were independent from each other, which is obviously unreal to real-life cases. In this paper, we attempt to address this challenge by introducing a novel co-ranking framework, named MutuRank. It makes full use of the mutual influence between relations and actors to transform the multi-relational network to the single-relational network. We then present GMM-NK (Gaussian Mixture Model with Neighbor Knowledge) based on local consistency principle to enhance the performance of spectral clustering process in discovering overlapping communities. Experimental results on both synthetic and real-world data demonstrate the effectiveness of the proposed method.