Community Detection Based on Co-regularized Nonnegative Matrix Tri-Factorization in Multi-view Social Networks

Longqi Yang, Liangliang Zhang, Zhisong Pan, Guyu Hu, Yanyan Zhang

Abstract: Social network is a hot issue in recent years. In this field, data with multiple views or data from multiple sources are referred to as multi-view data. Due to the uncertain quality of data source, single-view method often leads to unstable performance in community discovery. However, to combine various numbers of views to improve community detection performance is a challenge. In this paper, we propose a method called CoNMTF (Co-regularized Nonnegative Matrix Tri-Factorization). A relaxed pairwise regularization is introduced to integrate multiview adjacency data. Under this framework, we propose an iterative algorithm and prove its correctness and convergence. Experimental results on both synthetic datasets and real-world datasets demonstrate that it outperforms the-state-of-art algorithms in terms of accuracy and NMI.