Low-Rank Estimation Based Evolutionary Clustering For Community Detection In Temporal Networks

Many real-world systems can be represented by networks. One common approach to characterizing the organization of networks is community detection. A lot of work has been conducted in community detection of static networks. However, most real systems are time dependent and modeled by temporal networks with a structure that evolves across time. In this paper, a low-rank approximation based evolutionary clustering approach is introduced to detect and track the community structure of temporal networks. The proposed approach provides robustness to outliers and results in smoothly evolving cluster assignments through joint low-rank approximation and subspace learning. Moreover, a cost function is introduced to track changes in the community structure across time. The performance of the proposed approach is validated on both simulated and real temporal networks.