Detecting Location-Centric Communities Using Social-Spatial Links with Temporal Constraints

Community detection on social networks typically aims to cluster users into different communities based on their social links. The increasing popularity of Location-based Social Networks offers the opportunity to augment these social links with spatial information, for detecting location-centric communities that frequently visit similar places. Such location-centric communities are important to companies for their location-based and mobile advertising efforts. We propose an approach to detect location-centric communities by augmenting social links with both spatial and temporal information, and demonstrate its effectiveness using two Foursquare datasets. In addition, we study the effects of social, spatial and temporal information on communities and observe the following: (i) augmenting social links with spatial and temporal information results in location-centric communities with high levels of check-in and locality similarity; (ii) using spatial and temporal information without social links however leads to communities that are less location-centric.