A Parallel Community Detection Algorithm Based on Incremental Clustering in Dynamic Network

Dynamic community detection is a key method for the research of network evolution. However, most existing dynamic community detection algorithms are time-consuming in dealing with large-scale networks. Moreover, most current parallel community detection algorithms are static and they ignore the changes of network structure over time. In this paper, we propose a novel parallel algorithm based on incremental vertices, which is able to process large-scale dynamic networks, called PICD. In PICD algorithm, the revised Parallel Weighted Community Clustering (PWCC) metric is conductive to a convenient calculation, which is more sensitive to community structure compared to other metrics. The PICD approach consists of two main steps. Firstly, it identifies the incremental vertices in the dynamic network. Secondly, it maximizes the PWCC of the entire network by merely adjusting the community membership of incremental vertices to capture community structure in high quality. The results of experiments on both the synthetic and real world networks demonstrate that the PICD algorithm achieves a higher accuracy and efficiency. Moreover, it performs more stable than most of the baseline methods. The experiments also show that PICD algorithm takes an almost linear time with the growth of the network scale.