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Interacting model of rumor propagation and behavior spreading in multiplex networks

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  • Zhang, Yaming
  • Su, Yanyuan
  • Weigang, Li
  • Liu, Haiou

Abstract

Rumor propagation and behavior spreading are usually closely coupled with each other and the interaction will have great influence on the spreading dynamics. In this paper, we propose a novel interacting model of rumor propagation and behavior spreading in multiplex networks. Specifically, coupled reinforcements are introduced in the derived mean-field equations to describe the interplay between these two dynamical processes. Then the basic reproduction number and the final sizes of rumor and behavior spreading are estimated. Monte Carlo simulations results show that the interacting model are much more consistent with the real data than the classical model. Besides, the attractiveness of rumor and behavior and couple reinforcements are crucial factors affecting the interactive spreading processes. Especially, influenced by the coupled reinforcements, any attractiveness increasing can enhance maximum influences and final spreading sizes of rumor and behavior simultaneously. What’s more, the coupled reinforcements will extend the duration of rumor and behavior spreading. Moreover, the reinforcement of spreaders or infecteds to promote spreading is far stronger than that of stiflers and recovered to suppress spreading. Interestingly, we also find that the final size of rumor propagation is larger than that of behavior spreading, while the range of the final size variation resulting from the coupled reinforcements is opposite. This work may shed some lights on understanding the interaction between rumor and behavior and suggest a promising way to control rumors and irrational behaviors.

Suggested Citation

  • Zhang, Yaming & Su, Yanyuan & Weigang, Li & Liu, Haiou, 2019. "Interacting model of rumor propagation and behavior spreading in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 168-177.
    • Handle: RePEc:eee:chsofr:v:121:y:2019:i:c:p:168-177
    DOI: 10.1016/j.chaos.2019.01.035
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    Cited by:

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    2. Gündüç, Semra & Eryiğit, Recep, 2021. "Time dependent correlations between the probability of a node being infected and its centrality measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    3. Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

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