Context-Aware Community: Integrating Contexts with Contacts for Proximity-Based Mobile Social Networking

Sensor-equipped mobile devices have allowed users to participate in various social networking services while they are on the go. We focus on proximity-based mobile social networking environments where users share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile social networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom) as the connection, and the space and time related to the shared information as the social contexts. To model the potential information influences, we construct an influence graph by integrating the social contexts into the contacts of mobile users. Further, we propose a twophase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.