Joint orthogonal symmetric non-negative matrix factorization for community detection in attribute network

Abstract

Community detection is an important and challenging task in complex attribute network analysis. Symmetric non-negative matrix factorization-based methods have become promising because of their excellent ability to extract low-dimensional representations of attribute networks (which are characterized by their adjacency and attribute data). In this paper, we propose a novel community detector called joint symmetric non-negative matrix factorization model, in which the attribute homogeneity and topology similarities of an attribute network are characterized in a unified framework. An orthogonal constraint is imposed on the factor matrix to improve the accuracy of nodes’ affiliations to communities. Furthermore, a novel multi-order graph regularization was developed to preserve the intrinsic geometric structure, and an eigenvector centrality-based enhancement strategy was established to explore more comprehensive adjacency information. Extensive experiments on community detection tasks demonstrate that the proposed method performs significantly better than existing state-of-the-art methods in most benchmark complex networks.