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Targeted Influential Nodes Selection in Location-Aware Social Networks

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  • Susu Yang
  • Hui Li
  • Zhongyuan Jiang

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.

Suggested Citation

  • Susu Yang & Hui Li & Zhongyuan Jiang, 2018. "Targeted Influential Nodes Selection in Location-Aware Social Networks," Complexity, Hindawi, vol. 2018, pages 1-10, November.
    • Handle: RePEc:hin:complx:6101409
    DOI: 10.1155/2018/6101409
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    References listed on IDEAS

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    1. Yufei Liu & Dechang Pi & Lin Cui, 2017. "Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo," Complexity, Hindawi, vol. 2017, pages 1-16, December.
    2. Sen Su & Xiao Li & Xiang Cheng & Chenna Sun, 2018. "Location†aware targeted influence maximization in social networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(2), pages 229-241, February.
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