Community Detection in Temporal Networks Using Triple Nonnegative Matrix Factorization

Analyzing temporal networks can uncover dynamic evolution and characterize the properties of the networks. This paper proposes a novel temporal community detection model using triple nonnegative matrix factorization. Node weight matrices are introduced for targeting central nodes of communities and reducing number of nodes that have unobvious propensities of belonging to communities, which improves the algorithm performance of community detection. Community membership temporal smoothness constraint is added to discover latent structure and evolutionary behaviors of temporal networks. We then propose a gradient descent algorithm to optimize objective function. Experimental results on synthetic and real benchmarked networks show the effectiveness of detecting communities and finding their temporal changes.