Geo-Social Influence Spanning Maximization

Jianxin Li, Timos Sellis, J. Shane Culpepper, Zhenying He, Chengfei Liu, Junhu Wang
2018 2018 IEEE 34th International Conference on Data Engineering (ICDE)  
Influence maximization is a recent well-studied problem developed for identifying a small set of users that are most likely to "influence" the maximum number of users in a social network. The problem has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the social users, but location is an important factor in targeted marketing. In this paper, we propose and investigate the
more » ... roblem of influence maximization in location-aware social networks, or, more generally, Geo-social Influence Spanning Maximization. Given a query q composed of a region R, a regional acceptance rate ρ, and an integer k as seed selection budget, our aim is to find the maximum geographic spanning regions (MGSR). We refer to this as the MGSR problem. Our approach differs from previous work as we focus more on identifying the maximum spanning geographical regions in the region R, rather than just the number of activated users in the given network like the traditional influence maximization problem [14] , and in the query region like the location aware influence maximization problem [17] . This research can advance the effect of online campaigns in viral marketing by considering the locations of social users. To address the MGSR problem, we first show it is an NP-Hard problem. Next, we present a greedy algorithm with a 1 − 1/e approximation ratio to solve the problem and further improve its efficiency by developing an upper bound based approach. Then, we propose the OIR*-tree index, which is a hybrid index combining ordered influential node lists with an R*-tree. We show that our index based approach is significantly more efficient than the greedy algorithm and the upper bound based algorithm, especially when k is large. Finally, we evaluate the performance for all of the proposed approaches using three real datasets. ✦ • Jianxin Li is with the
doi:10.1109/icde.2018.00245 dblp:conf/icde/LiSCHLW18 fatcat:5b63clcatbd3pbdcbdhko6utw4