GNMFO_TW: Graph Regularized Non-negative Matrix Factorization for Overlapping Community Discovery by Using Three-Way

Community detection is one of the important means of complex network analysis. The current community detection tasks focus on hard community division. However, the analysis of overlapping community structures remains challenging research for real applications. This article proposes a new graph regularized overlapping community detection method with the topological structure information, utilizing effective theoretical three-way decisions for handling uncertainty knowledge. That the model integrates the essential structural information within the network is implemented by using the idea of subspace clustering. Based on node structural similarity, three-way decisions are utilized to determine the overlapping structures and nodes in them. This model not only obtains node community membership, but also find the overlapping community structure. Compared with the state-and-the-art overlapping community detection methods on artificial and real networks, the experimental results show that the proposed method has competitive performance.