A novel dynamic community detection algorithm based on modularity optimization

Dynamic community detection has been an attractive topic due to its ability to reveal the evolutionary trends over time. However, existing dynamic community detection algorithms suffer from several disadvantages. Some make strong assumptions about the generation of communities, or require priori knowledge. In this paper, we propose a novel algorithm, dynamic Louvain method, to detect communities in temporal networks based on modularity optimization. The basic motivation is that the communities across different time steps should smoothly evolve. When partitioning temporal networks at a given time step, we should take historical network structure into consideration. Besides, this algorithm makes no assumption about the generation of communities, and is able to decide the number of communities automatically. This novel algorithm is applied to the temporal financial networks, and numerical evaluations show that this novel algorithm could obtain better partitions, compared with other state-of-art algorithms.