Targeted Influential Nodes Selection in Location-Aware Social Networks

Susu Yang, Hui Li and Zhongyuan Jiang

Complexity, 2018, vol. 2018, 1-10

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6101409

DOI: 10.1155/2018/6101409

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