Discrete Overlapping Community Detection with Pseudo Supervision

Community detection is of significant importance in understanding the structures and functions of networks. Recently, overlapping community detection has drawn much attention due to the ubiquity of overlapping community structures in real-world networks. Nonnegative matrix factorization (NMF), as an emerging standard framework, has been widely employed for overlapping community detection, which obtains nodes' soft community memberships by factorizing the adjacency matrix into low-rank factor matrices. However, in order to determine the ultimate community memberships, we have to post-process the real-valued factor matrix by manually specifying a threshold on it, which is undoubtedly a difficult task. Even worse, a unified threshold may not be suitable for all nodes. To circumvent the cumbersome post-processing step, we propose a novel discrete overlapping community detection approach, i.e., Discrete Nonnegative Matrix Factorization (DNMF), which seeks for a discrete (binary) community membership matrix directly. Thus DNMF is able to assign explicit community memberships to nodes without post-processing. Moreover, DNMF incorporates a pseudo supervision module into it to exploit the discriminative information in an unsupervised manner, which further enhances its robustness. We thoroughly evaluate DNMF using both synthetic and real-world networks. Experiments show that DNMF has the ability to outperform state-of-the-art baseline approaches.