User-centric approach of detecting temporary community

The proliferation and widespread of social web provide rich data about peoples' activities. For instance, geographical position and timestamps of each event can be obtained thanks to mobile devices and location-based services. Basically it sounds well that people, who used to be frequently at the same place and the same time period, are very likely to be socially related. Therefore, it is worth-noting that tracking user's position over time may help discovering interesting patterns, which can be used to enhance community structure. The goal of this paper is to track and collect users' spatiotemporal activities events in order to unveil homogeneous groups that could not appear in a unique social network. In this respect, we propose a community detection approach from spatiotemporal aspects. First, we use ST-DBSCAN clustering algorithm to identify clusters corresponding to temporary communities that we called perspective. Then, we apply different random graph models to establish relationships between pairs of individuals. This real-time detecting algorithm is done to track the dynamic evolution of communities within a sequence of time windows. Finally, we test our proposed method on a real-life datasets in order to analyze the soundness and feasibility of this technique.