IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v608y2022ip1s0378437122008627.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122008627
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128304?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramírez-Correa, Patricio & Grandón, Elizabeth E. & Rondán-Cataluña, F. Javier, 2020. "Users segmentation based on the Technological Readiness Adoption Index in emerging countries: The case of Chile," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    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.
    3. Hao Wu & Xiangyi Meng & Michael M. Danziger & Sean P. Cornelius & Hui Tian & Albert-László Barabási, 2022. "Fragmentation of outage clusters during the recovery of power distribution grids," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    4. Peng, Hao & Peng, Wangxin & Zhao, Dandan & Wang, Wei, 2020. "Impact of the heterogeneity of adoption thresholds on behavior spreading in complex networks," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    5. Yang, Qiwen & Zhu, Xuzhen & Tian, Yang & Wang, Guanglu & Zhang, Yuexia & Chen, Lei, 2021. "The influence of heterogeneity of adoption thresholds on limited information spreading," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    6. Wen-Juan Xu & Chen-Yang Zhong & Fei Ren & Tian Qiu & Rong-Da Chen & Yun-Xin He & Li-Xin Zhong, 2020. "Evolutionary dynamics in financial markets with heterogeneities in strategies and risk tolerance," Papers 2010.08962, arXiv.org.
    7. Longbing Cao & Chengzhang Zhu, 2022. "Personalized next-best action recommendation with multi-party interaction learning for automated decision-making," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-22, January.
    8. Cui, Yajuan & Wei, Ruichen & Tian, Yang & Tian, Hui & Zhu, Xuzhen, 2022. "Information propagation influenced by individual fashion-passion trend on multi-layer weighted network," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    9. Qu, Junyi & Liu, Ying & Tang, Ming & Guan, Shuguang, 2022. "Identification of the most influential stocks in financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    10. Li, Jiachen & Li, Wenjie & Gao, Feng & Cai, Meng & Zhang, Zengping & Liu, Xiaoyang & Wang, Wei, 2024. "Social contagions on higher-order community networks," Applied Mathematics and Computation, Elsevier, vol. 478(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008627. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.