Detecting and refining overlapping regions in complex networks with three-way decisions

Abstract

The identification of communities is crucial to an understanding of the structural and functional properties of a complex network. Many methods and algorithms have been developed to detect overlapping communities. A problem that has not been addressed satisfactorily is the relationship or difference between vertices in overlapping regions during the formation and growth of communities. This paper investigates methods that not only detect the overlapping communities but also refine the overlapping regions. We give a three-way representation of a community by using interval sets and re-formalize the problem of community detection as three-way clustering. We suggest four macro types and eight micro types of vertices to characterize members in overlapping regions for their refinement. We propose an overlapping community detection algorithm by classifying the vertices into core vertices, bone vertices, and trivial vertices. The main strategy of this algorithm is to find an initial cluster core, to expand the core to a preliminary community according to a new fitness function, and to merge trivial vertices based on three-way decision strategies. The experimental results on both real-world social networks and computer-generated artificial networks show the effectiveness and efficiency of the proposed methods.