A label-based evolutionary computing approach to dynamic community detection

Dynamic community detection is the process to discover the structure of and determine the number of communities in dynamic networks consisting of a series of temporal network snapshots. Due to the time-varying characteristics of such networks, community detection must consider both the quality of the community structure and the temporal cost that quantifies the difference between the current network snapshot and previous ones. In this paper, we propose a label-based multi-objective optimization algorithm for dynamic community detection, which employs a genetic algorithm to optimize two objectives, i.e. clustering quality and temporal cost. A label propagation method is designed and used to initialize the network's communities and restrict the conditions of the mutation process to further improve the detection efficiency and effectiveness. We conduct experiments on both synthesized and empirical datasets, and extensive results illustrate that the proposed method outperforms a state-of-the-art algorithm in terms of detection quality and speed, which sheds light on its wide applications to various complex networks with dynamic structures such as rapidly growing online social networks.