Overlapping Community Detection Algorithm Based on Coarsening and Local Overlapping Modularity

Abstract

Community detection is an important research direction in the field of complex network analysis. It aims to discover community structures in complex networks. Algorithms based on dynamic distance mechanism can find stable communities with various shapes. However, they still cannot discover overlapping or outlier communities. This paper proposes an overlapping community discovery algorithm based on coarsening and local overlapping modularity. First, to reduce the running time, a new equation for computing the local overlapping modularity increment is derived. This equation finds the overlapping communities, accurately and quickly. Second, a new similarity measuring strategy is designed to reduce the number of outlier communities. The experiments on artificial and real datasets show that the proposed algorithm can discover the overlapping communities, accurately and efficiently.