Efficient Sample Location Selection for Query Zone in Geo-Social Networks

Abstract

While promoting a business or activity in geo-social networks, the geographical distance between its location and users is critical. Therefore, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM heavily relies on the sample location selection. Specifically, the online seeding performance is sensitive to the distance between the promoted location and its nearest sample location, and the offline precomputation performance is sensitive to the number of sample locations. However, there is no work to fully study the problem of sample location selection for DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted location always adheres to the distribution of users (query zone). Then, we propose two efficient location sampling approaches based on facility location analysis, which is one of the most well-studied areas of operations research, and these two approaches are denoted by Facility Location based Sampling (FLS) and Conditional Facility Location Based Sampling (CFLS), respectively. FLS conducts one-time sample location selection, and CFLS extends the one-time sample location selection to a continuous process, so that an online advertising service can be started immediately without sampling a lot of locations. Our experimental results on two real datasets demonstrate the effectiveness and efficiency of the proposed methods. Specifically, both FLS and CFLS can achieve better performance than the existing sampling methods for the DAIM problem, and CFLS can initialize the online advertising service in a matter of seconds and achieve better objective distance than FLS after sampling a large number of sample locations.