Discovering Social Community Structures Based on Human Mobility Traces

Abstract

We consider a community detection problem in a social network. A social network is composed of smaller communities; that is, a society can be partitioned into different social groups in which the members of the same group maintain stronger and denser social connections than individuals from different groups. In other words, people in the same community have substantially interdependent social characteristics, indicating that the community structure may facilitate understanding human interactions as well as individual’s behaviors. We detect the social groups within a network of mobile users by analyzing the Bluetooth-based encounter history from a real-life mobility dataset. Our community detection methodology focuses on designing similarity measurements that can reflect the degree of social connections between users by considering tempospatial aspects of human interactions, followed by clustering algorithms. We also present two evaluation methods for the proposed schemes. The first method relies on the natural properties of friendship, where the longevity, frequency, and regularity characteristics of human encounters are considered. The second is a movement-prediction-based method which is used to verify the social ties between users. The evaluation results show that the proposed schemes can achieve high performance in detecting the social community structure.