Overlapping Kernel-based community detection with node attributes

Community Detection is a fundamental task in the field of Social Network Analysis, extensively studied in literature. Recently, some approaches have been proposed to detect communities distinguishing their members between kernel that represents opinion leaders, and auxiliary who are not leaders but are linked to them. However, these approaches suffer from two important limitations: first, they cannot identify overlapping communities, which are often found in social networks (users are likely to belong to multiple groups simultaneously); second, they cannot deal with node attributes, which can provide important information related to community affiliation. In this paper we propose a method to improve a well-known kernel-based approach named Greedy-WeBA (Wang et al., 2011) and overcome these limitations. We perform a comparative analysis on three social network datasets, Wikipedia, Twitter and Facebook, showing that modeling overlapping communities and considering node attributes strongly improves the ability of detecting real social network communities.