Targeted Influential Nodes Selection in Location-Aware Social Networks. (1st November 2018)

• Record Type: Journal Article
• Authors: Yang, Susu; Li, Hui; Jiang, Zhongyuan
• Other Names: Li Jianxin Guest Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-11-01
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/6101409
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Given a target area and a location-aware social network, the location-aware influence maximization problem aims to find a set of seed users such that the information spread from these users will reach the most users within the target area. We show that the problem is NP-hard and present an approximate algorithm framework, namely, TarIM-SF, which leverages on a popular sampling method as well as spatial filtering model working on arbitrary polygons. Besides, for the large-scale network we also present a coarsening strategy to further improve the efficiency. We theoretically show that our approximate algorithm can provide a guarantee on the seed quality. Experimental study over three real-world social networks verified the seed quality of our framework, and the coarsening-based algorithm can provide superior efficiency.