Community Detection Based on Regularized Semi-Nonnegative Matrix Tri-Factorization in Signed Networks

Community detection is a fundamental task in the social network analysis field, which is beneficial for many real-world applications such as recommendation systems and telephone fraud detection. Community detection in unsigned networks has been extensively studied, however, few works focus on community detection in signed networks. Under this background, we propose a framework based on regularized semi-nonnegative matrix tri-factorization which maps the signed network from high-dimensional space to low-dimensional space, such that the communities of the signed network can be derived. In addition, to improve the detection accuracy, we introduce a graph regularization to distribute the pair of nodes which are connected with negative links into different communities. The experimental results on both synthetic datasets and real-world datasets verify the effectiveness of the proposed method.