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Effective identification of multiple influential spreaders by DegreePunishment

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  • Wang, Xiaojie
  • Su, Yanyuan
  • Zhao, Chengli
  • Yi, Dongyun

Abstract

With the rapid development of social networks, how to effectively identify a small group of nodes to maximize their spreading influence becomes a crucial topic. Traditional centrality-based methods are often very simple but not so effective compared to other complex methods. In this paper, we propose a heuristic method to select spreaders sequentially by carrying out a punishing strategy to the neighbors of those already selected spreaders. We use the Susceptible–Infected–Recovered (SIR) model to evaluate the performance by considering the number of infected nodes in the end. Experiments on four real networks show that our method outperforms traditional centrality-based methods and several heuristic ones.

Suggested Citation

  • Wang, Xiaojie & Su, Yanyuan & Zhao, Chengli & Yi, Dongyun, 2016. "Effective identification of multiple influential spreaders by DegreePunishment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 238-247.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:238-247
    DOI: 10.1016/j.physa.2016.05.020
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    References listed on IDEAS

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    Cited by:

    1. Yu, Senbin & Gao, Liang & Xu, Lida & Gao, Zi-You, 2019. "Identifying influential spreaders based on indirect spreading in neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 418-425.
    2. Salavati, Chiman & Abdollahpouri, Alireza & Manbari, Zhaleh, 2018. "BridgeRank: A novel fast centrality measure based on local structure of the network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 635-653.
    3. Wang, Jing & Ma, Xiao-Jing & Xiang, Bing-Bing & Bao, Zhong-Kui & Zhang, Hai-Feng, 2022. "Maximizing influence in social networks by distinguishing the roles of seeds," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

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