Mining community and inferring friendship in mobile social networks

Along with the rapidly growth of mobile terminals and wireless technologies, mobile social networking services are very popular with peoples. Recently many mobile social platforms based on location-based service are developed to allow users to share their check-ins and events with friends. Check-ins data in location-based mobile social networks as well as call detail records (CDR) in mobile communication network may provide insight into community structure, relationships and members in the network. In this paper, we study the problem of community detection and friendship prediction in mobile social networks. We have presented a method to find community structure built on combination entropy, and evaluate modularity of a virtual campus mobile network (V-Net). The outcomes demonstrate that the proposed algorithm mine meaningful communities according to users׳ registration. We investigate the potential friendship among users by taking into account both users׳ links with friends and their check-ins at various positions in Gowalla. This work describes the probability distributions of friendships per number of friends, number of check-ins and number of visited places. The findings confirm that our approaches achieve well performance with aggregated features of user similarity and place entropy than other methods. Moreover, members reveal different social properties in the two networks, in the V-Net influence users tend to hold community together, while in Gowalla community members are likely to visit the common positions.