OCMiner: A density-based overlapping community detection method for social networks

Abstract

Community detection is an important task for identifying the structure and function of complex networks. The task is challenging as communities often show overlapping and hierarchical behavior, i.e., a node can belong to multiple communities, and multiple smaller communities can be embedded within a larger community. Moreover, real-world networks often contain communities of arbitrary size and shape, along with outliers. This paper presents a novel density-based overlapping community detection method, OCMiner, to identify overlapping community structures in social networks. Unlike other density-based community detection methods, OCMiner does not require the neighborhood threshold parameter (ε) to be set by the users. Determining an optimal value for ε is a longstanding and challenging task for density-based clustering methods. Instead, OCMiner automatically determines the neighborhood threshold parameter for each node locally from the underlying network. It also uses a novel distance function which utilizes the weights of the edges in weighted networks, besides being able to find communities in un-weighted networks. The efficacy of the proposed method has been established through experiments on various real-world and synthetic networks. In comparison to the existing state-of-the-art community detection methods, OCMiner is computationally faster, scalable to large-scale networks, and able to find significant community structures in social networks.