Detecting Hierarchical Structure of Community Members by Link Pattern Expansion Method

Abstract

Community structure is an important property of complex networks, which is generally described as densely connected nodes and similar patterns of links. Hierarchy is a common property of networks. Different members have different belonging coefficients to the community, e.g. core members and boundary members, who are at different levels in the hierarchy of community. In this paper, a novel structure is presented, called hierarchical structure of members (HSM), which shows the relationships among members and multi-resolution of the community. A hierarchical link-pattern expansion method is proposed to detect HSM. First, we use the most similar link patterns to detect the seed communities which include both clique structures and star structures. Next, we define the influence between members to expand the community hierarchically. The experiment explores the hierarchical structure of members and the comparison with competitive algorithms on realworld networks demonstrates our method has stronger ability to detect communities.