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Social contagion with negative feedbacks

Author

Listed:
  • Ruan, Zhongyuan
  • Zhang, Lina
  • Shu, Xincheng
  • Xuan, Qi

Abstract

Real social contagion processes usually accompany feedback behaviors, such as people may give comments after they adopt a new product or watch a new movie, etc. In this paper, we extend the traditional threshold model that has been widely adopted for studying social contagion phenomena by incorporating the negative-feedback mechanism. In our model, nodes may give negative feedbacks with a certain probability after they become adopters, and correspondingly, the threshold of their susceptible neighbors will increase. By extensive simulations and mean-field analysis, we find that, if nodes give negative feedbacks with high possibility immediately after they become adopters, the contagion process could be effectively suppressed with only slight disturbances to the system, i.e., only a small fraction of nodes give negative feedbacks finally.

Suggested Citation

  • Ruan, Zhongyuan & Zhang, Lina & Shu, Xincheng & Xuan, Qi, 2022. "Social contagion with negative feedbacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    • Handle: RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008627
    DOI: 10.1016/j.physa.2022.128304
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    References listed on IDEAS

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    1. Alessandro Rizzo & Maurizio Porfiri, 2016. "Innovation diffusion on time-varying activity driven networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(1), pages 1-8, January.
    2. Zhao-Hua Lin & Mi Feng & Ming Tang & Zonghua Liu & Chen Xu & Pak Ming Hui & Ying-Cheng Lai, 2020. "Non-Markovian recovery makes complex networks more resilient against large-scale failures," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    3. Panagiotis D Karampourniotis & Sameet Sreenivasan & Boleslaw K Szymanski & Gyorgy Korniss, 2015. "The Impact of Heterogeneous Thresholds on Social Contagion with Multiple Initiators," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-15, November.
    4. Alessandro Rizzo & Maurizio Porfiri, 2016. "Innovation diffusion on time-varying activity driven networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(1), pages 1-8, January.
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