Dynamic Community Detection Using Nonnegative Matrix Factorization

Abstract

Community detection is of great importance in the study of complex networks, which motivates a body of new work in this domain. However, almost all networks change over time; traditional methods for static networks are not able to track evolutionary behaviors in temporal networks. To address this problem, we present a novel dynamic community detection model ENMF using nonnegative matrix factorization (NMF), which can not only track the temporal evolutions but also maintain the quality of detecting communities. Specifically, we propose gradient descent algorithm to optimize object function and evaluate the performance of the algorithm on one synthetic datasets. The results show that our proposed model outperforms other NMF methods.