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Layered SIRS model of information spread in complex networks

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  • Zhang, Yuexia
  • Pan, Dawei

Abstract

In complex network research, the infectious disease model is often used to study transmission mechanisms and interference factors of information; consequently, they are essential for the prediction and control of information transmission. The conventional SIRS epidemic model has a wide range of applications and is theoretically mature. However, it does not stratify the nodes in a network, and fails to reflect the characteristics of different nodes. To solve this problem, we propose a layered SIRS information transmission model (L-SIRS) . Depending on the node influence, we assign the nodes in the network to high- and low-influence layers and establish an intra- and inter-layer information transmission mechanism. The transmission threshold and equilibrium point of this model are analyzed theoretically. To reduce the transmission of online public opinions, two kinds of information transmission interference strategies, namely, information blocking and information dredging, are designed to study their influence on information transmission. Finally, combine with practice, the simulation results indicate that the L-SIRS model can more accurately describe network information transmission and both information blocking and information dredging can effectively inhibit the spread of information.

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  • Zhang, Yuexia & Pan, Dawei, 2021. "Layered SIRS model of information spread in complex networks," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    • Handle: RePEc:eee:apmaco:v:411:y:2021:i:c:s0096300321006135
    DOI: 10.1016/j.amc.2021.126524
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    References listed on IDEAS

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    Cited by:

    1. Chakir, Yassine, 2023. "Global approximate solution of SIR epidemic model with constant vaccination strategy," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    2. Zhu, Hongmiao & Jin, Zhen, 2023. "A dynamics model of knowledge dissemination in a WeChat Group from perspective of duplex networks," Applied Mathematics and Computation, Elsevier, vol. 454(C).

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