Abstract not found

Abstract: The study of community structures is a hot spot for many inhomogeneous networks. Modularity plays an important role in this area, because it is a criterion for community detection, and a basis for community detection algorithms. Although commonly used in papers concerning community structures, modularity is seldom fully studied. In this paper, we investigate problems with the properties of modularity as defined by Newman and we propose a modularity normalized for number of groups as well as a hybrid modularity that improves on properties that reflect the interactions among communities. We also illustrate the basic flowchart of a “bottom-up merging” community detection strategy based on the properties of modularity, and explore a detection algorithm inspired by hybrid modularity. The central idea of “Community Structure ” is widely used in the study of social, biological and technical networks, among others. It represents an important future direction of complex networks research. [1-8] In order to evaluate community structure, several measurements based on network density have been developed. [9] Modularity is