Discovering Communities in Social Networks Using Topology and Attributes

Many online social networks such as Facebook, LinkedIn and MySpace have become increasingly important. These social networks are rich in information about entities like hobbies, demographic information, friendship, and other attributes. This information can be used extensively for network analysis. One of the most important problems in social network analysis is community detection. The community detection problem is closely related to graph clustering. Most of the existing graph clustering algorithms employ only the structure of a graph to find highly connected components. These algorithms ignore nodes´ attributes that can help in improving the quality of the clustering. In this paper, we propose a clustering algorithm which clusters a graph by incorporating both the topological structure of the graph as well as attribute information. The aim is to find clusters such that the nodes in each cluster are similar in the attribute space. In terms of social networks, we are looking to find communities where the members of the same community have similar profiles. The method was evaluated using real and synthetic graph datasets. The experimental results demonstrate the effectiveness of the proposed method.