Community detection algorithm based on nonnegative matrix factorization and pairwise constraints

Abstract Community detection is a critical issue in the field of complex networks. Nonnegative matrix factorization (NMF) has been one of the hot research topics in community detection. In the real world, only topology information is inadequate to detect community structure accurately. To utilize the limited supervised information effectively, we propose two algorithms for community detection, which combine NMF or (Symmetric NMF) with pairwise constraints to deal with linearly separable data and nonlinearly separable data, respectively. Currently, most of NMF-based community detection methods need the number of communities in advance or searching among all candidates with different K . To address this issue, we use singular value decomposition to reveal the number of communities automatically. Experimental results demonstrate that the proposed methods can produce higher clustering performance compared with some state-of-the-art methods.