Detecting Overlapping Communities in Location-Based Social Networks

With the recent surge of location-based social networks (LBSNs, e. g., Foursquare, Facebook Places), huge amount of digital footprints about users' locations, profiles as well as their online social connections become accessible to service providers. Different from social networks (e. g., Flickr, Facebook) which have explicit groups for users to subscribe or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection approach is needed so as to enable applications such as direct marketing, group tracking, etc. The diversity of people's interests and behaviors when using LBSNs suggests that their community structures overlap. In this paper, based on the user-venue check-in relationship and user/venue attributes, we come out with a novel multi-mode multi-attribute edge-centric co-clustering (M-2 Clustering) framework to discover the overlapping communities of LBSNs users. By employing inter-mode/intra-mode features, the proposed framework is able to group like-minded users from different social perspectives. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset of 266,838 users with 9,803,764 check-ins over 2,477,122 venues worldwide.