A New SOP for Accurate and Efficient Community Detection

Abstract

Community is one of the most important features in social networks. There were many traditional methods in the literature to detect communities in network science and sociological studies, but few were able to identify the statistical significance of the detected communities. Even worse, these methods were computationally infeasible for networks with large numbers of nodes and edges. In this talk, we introduce a new SOP for detecting communities in a social network accurately and efficiently. It consists of four main steps. First, a screening stage is proposed to roughly divide the whole network into communities via complement graph coloring. Then a likelihood-based statistical test is introduced to test for the significance of the detected communities. Once these significant communities are detected, another likelihood-based statistical test is introduced to check for the focus centrality of each community. Finally, a metaheuristic swarm intelligence based (SIB) method is proposed to fine tune the range of each community from its original circular setting. Some famous networks are used as empirical data to demonstrate the process of this new SOP.