Multi-Mode Social Network Clustering via Non-Negative Tri-Matrix Factorization With Cluster Indicator Similarity Regularization

Community discovery algorithms are important aspects of network science, especially as social network structures become more complex. Multi-mode social networks have recently become a challenging and popular topic in this field. At present, inner-mode relationship is mainly considered in community discovery algorithms for social networks. Thus, the effect of the these methods is not well in clustering as the intra-mode relationship is not considered in the clustering methods. In this paper, we propose a flexible and robust clustering framework, MRTA (the Multi-Similarity Regular Tri-Factorization Algorithm), based on non-negative tri-matrix factorization. MRTA has several advantages over the existing methods. First, it achieves more consistent clustering results based on cluster indicator of inner-mode and intra-mode relationships of multi-mode networks. Second, it can simultaneously cluster multiple modes, which is impossible for single-mode clustering algorithms. Finally, it provides a multi-mode clustering solution that is more robust to noise. We perform an efficient iterative update algorithm, and theoretically prove its accuracy. Extensive experimental results of a variety of real and synthetic networks demonstrate the effectiveness of our approach.