1. Geo-Social Influence Spanning Maximization.
- Author
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Li, Jianxin, Sellis, Timos, Culpepper, J. Shane, He, Zhenying, Liu, Chengfei, and Wang, Junhu
- Subjects
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ONLINE social networks , *INTEGRATED circuits , *SOCIAL media , *SOCIAL influence , *INTERNET marketing - Abstract
Influence maximization is a recent but well-studied problem which helps identify 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 users, but location is an important factor in targeted marketing. In this paper, we propose and investigate the problem of influence maximization in location-aware social networks, or, more generally, Geo-social Influence Spanning Maximization. Given a query $q$
composed of a region $R$ as a 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 within a region $R$[14] . Our research approach can be effectively used for online marketing campaigns that depend on the physical location of social users. To address the MGSR problem, we first prove NP-Hardness. Next, we present a greedy algorithm with a $1-1/e$- Published
- 2017
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