ORTHOGONAL NONNEGATIVE MATRIX TRI-FACTORIZATION FOR COMMUNITY DETECTION IN MULTIPLEX NETWORKS

Networks provide a powerful tool to model complex systems. Recently, there has been a growing interest in multiplex networks as they can represent the interactions between a pair of nodes through multiple types of links, each reflecting a distinct type of interaction. One of the important tools in understanding network topology is community detection. Existing work on multiplex community detection mostly focuses on learning a common community structure across layers without taking the heterogeneity of the different layers into account. In this paper, we introduce a new multiplex community detection approach that can identify communities that are common across layers as well as those that are unique to each layer. The proposed algorithm employs Orthogonal Non-Negative Matrix Tri-Factorization to model each layer?s adjacency matrix as the sum of two low-rank matrix factorizations, corresponding to the common and private communities, respectively. The proposed algorithm is evaluated on both synthetic and real multiplex networks and compared to state-of-the-art techniques.