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Link cooperation effect of cooperative epidemics on complex networks

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  • Wang, Jun
  • Cai, Shimin
  • Wang, Wei
  • Zhou, Tao

Abstract

Epidemic spreading dynamic is usually used to describe the virus spread in the biology system and information diffusion in the social system. Previous studies indicated that multiple epidemics rarely evolve dependently, but constantly interact and coevolve. In this paper, a novel mathematical model is proposed to study the link cooperation effect of two epidemics cooperatively spreading on complex networks. The link cooperation effect means that the first epidemic is transmitted through this link, and the second epidemic will have a higher transmission probability when it is transmitted through the same link. We develop an edge-based compartmental method and can well predict the numerical simulations. We find the link cooperation effect promote the epidemic outbreak size. The phase transition is closely relates to the strength of the link cooperation effect and network topology. For the cooperate epidemics on ER networks, the phase transition is discontinuous for a stronger link cooperation effect, otherwise the phase transition is continuous. When cooperative epidemics spread on SF networks, the phase transition is always continuous because of the existence of a few hubs. Our developed theoretical method can reasonably predict the above phenomena that are obtained from the numerical simulations based on the proposed mathematical model.

Suggested Citation

  • Wang, Jun & Cai, Shimin & Wang, Wei & Zhou, Tao, 2023. "Link cooperation effect of cooperative epidemics on complex networks," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    • Handle: RePEc:eee:apmaco:v:437:y:2023:i:c:s0096300322006117
    DOI: 10.1016/j.amc.2022.127537
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    References listed on IDEAS

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    Cited by:

    1. Li, Wen-Jing & Chen, Zhi & Wang, Jun & Jiang, Luo-Luo & Perc, Matjaž, 2023. "Social mobility and network reciprocity shape cooperation in collaborative networks," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    2. Li, Xianghua & Zhen, Xiyuan & Qi, Xin & Han, Huichun & Zhang, Long & Han, Zhen, 2023. "Dynamic community detection based on graph convolutional networks and contrastive learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).

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