Community detection in signed networks based on extended signed modularity

Abstract

Community detection is important for analyzing and visualizing given networks. In real world, many complex systems can be modeled as signed networks composed of positive and negative edges. Although community detection in signed networks has been attempted by many researchers, studies for detecting detailed structures remain to be done. In this paper, we extend modularity for signed networks, and propose a method for optimizing our modularity, which is an efficient hierarchical agglomeration algorithm for detecting communities in signed networks. Based on the experiments with large-scale real world signed networks such as Wikipedia, Slashdot and Epinions, our method enables us to detect communities and inner factions inside the communities.