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Functional immunization of networks based on message passing

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  • Li, Shudong
  • Zhao, Dawei
  • Wu, Xiaobo
  • Tian, Zhihong
  • Li, Aiping
  • Wang, Zhen

Abstract

Network immunization has been widely adopted for restraining epidemic spreading. Majority of the existing results on identifying immunization targets and the measurements of their effectiveness are based purely on network topology. However the topological heuristic strategies neglect important features of the spreading dynamics and consequently may cannot yield reliable results. In this paper, we present a novel network immunization strategy based on explosive percolation and message passing, which considers both the network topology and epidemic dynamic. We compare its performance with the greedy strategy, topological heuristic strategy and random strategy. The results demonstrate the efficiency of our method on a variety of real-world examples.

Suggested Citation

  • Li, Shudong & Zhao, Dawei & Wu, Xiaobo & Tian, Zhihong & Li, Aiping & Wang, Zhen, 2020. "Functional immunization of networks based on message passing," Applied Mathematics and Computation, Elsevier, vol. 366(C).
    • Handle: RePEc:eee:apmaco:v:366:y:2020:i:c:s0096300319307209
    DOI: 10.1016/j.amc.2019.124728
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    Cited by:

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    7. Li, Shudong & Jiang, Laiyuan & Wu, Xiaobo & Han, Weihong & Zhao, Dawei & Wang, Zhen, 2021. "A weighted network community detection algorithm based on deep learning," Applied Mathematics and Computation, Elsevier, vol. 401(C).

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