Location Influence in Location-based Social Networks

Location-based social networks (LBSN) are social networks complemented with location data such as geo-tagged activity data of its users. In this paper, we study how users of a LBSN are navigatilig between locations sod based on this information we select the most influential locations. In contrast to existing works on influence maximization, we are not per se interested in selecting the users with the largest set of friends or the set of locations visited by the most users; instead, we introduce a notion of location inflaence that captures the ability of a set of locations to reach out geographically. We provide an exact on-line algorithm and a more memory-efficient but approximate variant based on the HyperLogLog sketch to maintain a. data structure called Influence Oracle that allows to efficiently find a top-k set of influential locations. Experiments show that our algorithms are efficient and scalable and that our new location influence notion favors diverse sets of locations with a large geograpldcal spread.