Overlapping community detection in large networks from a data fusion view

Abstract

Community detection is one of the most important problems in social network analysis in the context of the structure of the underlying graphs. Many researchers have proposed their own methods for discovering dense regions in social networks. Such methods are only designed with links of the underlying social network. However, with the development of recent applications, rich edge content can be available to give another view to the community detection process. In this study, we focus on improving community detection with the edge content in social networks. In order to regulate the effect of both linkage structure and edge content, we propose two feature integration strategies. Experiment results illustrate that the presence of edge content provides unprecedented opportunities and flexibility for the community detection process.