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Information propagation with individual attention-decay effect on activity-driven networks

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  • An, Xuming
  • Ding, Li
  • Hu, Ping

Abstract

More consideration on individual attribute or behavior has been recognized as the important segment for a comprehensive understanding of information propagation. In this paper, we investigate the impacts of individual attention-decay effect on dynamical process under the framework of activity-driven network. In order to explicitly describe the attention-decay, a modified model incorporating sub-infected (or decay) status based on SIR is proposed. In the current model, infected individuals may reduce their attentions towards information at a state-dependent decay rate formulated by threshold model, which is determined by the time-varying state of their neighbors. Whereafter, outbreak thresholds of information dynamics under different circumstances are obtained and the comparison with the classic model is also carried out accordingly. The results show that the introduction of attention-decay effect greatly interferes with the outbreak threshold and prevalence scale of information. Extensive numerical simulations are presented to illustrate the validity and efficiency of our theoretical results.

Suggested Citation

  • An, Xuming & Ding, Li & Hu, Ping, 2020. "Information propagation with individual attention-decay effect on activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    • Handle: RePEc:eee:phsmap:v:556:y:2020:i:c:s0378437120304180
    DOI: 10.1016/j.physa.2020.124815
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    References listed on IDEAS

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    Cited by:

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    2. Ping Yu & Peiwen Wang & Zhiping Wang & Jia Wang, 2022. "Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

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