Abstract
Community association is an important attribute of a social network because people may belong to varying groups with different characteristics at different times. Traditional community detection approaches often rely on a centralized server and are only useful for offline data analysis. In this paper, we propose and evaluate a distributed community detection approach that allows individual users to detect their own communities based on local observations. Our proposed template-matching method derives dynamic spatial and temporal characteristics of social communities by exploiting human's mobility patterns. Our template matching method allows users with similar moving patterns to be grouped together as one community. Our results using both simulation as well as real experiments demonstrate that our method can detect local communities effectively with high detection rate and low false positive rate.