Finding Geo-Social Cohorts in Location-Based Social Networks

Given a record of geo-tagged activities, how can we suggest groups, or cohorts of likely companions? A brute-force approach is to perform a spatio-temporal join over past activity traces to find groups of users recorded as moving together; yet such an approach is inherently unscalable. In this paper, we propose that we can identify and predict such cohorts by leveraging information on social ties along with past geo-tagged activities, i.e., geo-social information. In particular, we find groups of users that (i) form cliques of friendships and (ii) maximize a function of common pairwise activities on record among their members. We show that finding such groups is an NP-hard problem, and propose a nontrivial algorithm, COVER, which works as if it were enumerating maximal social cliques, but guides its exploration by a pruning-intensive activity driven criterion in place of a clique maximality condition. Our experimental study with real world data demonstrates that COVER outperforms a brute-force baseline in terms of efficiency and surpasses an adaptation of previous work in terms of prediction accuracy regarding groups of companions, including groups that do not appear in the training set, thanks to its use of a social clique constraint.