IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v149y2021ics0960077921004197.html
   My bibliography  Save this article

Feedback driven message spreading on network

Author

Listed:
  • Nian, Fuzhong
  • Liu, Jinshuo

Abstract

This paper focuses on the role of feedback mechanism on message propagation. In this paper, we study the effect of feedback on message propagation in terms of both the motivation of the communicator to propagate the message and the trust of the message recipient in the communicator, and we design a decay mechanism of motivation based on Newton's cooling law. Based on these considerations, we propose a model of message propagation based on the feedback mechanism. We verify the effect of feedback on message propagation by performing numerical simulations in the Watts-Strogatz (WS) and Barabasi-Albert (BA) networks. The simulation results show that the feedback mechanism leads to faster and more persistent message propagation and that the degree of influence on message propagation varies across different network structures. The simulation also results show that the feedback mechanism changes the structure of the social network and makes the nodes between the networks more closely connected.

Suggested Citation

  • Nian, Fuzhong & Liu, Jinshuo, 2021. "Feedback driven message spreading on network," Chaos, Solitons & Fractals, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:chsofr:v:149:y:2021:i:c:s0960077921004197
    DOI: 10.1016/j.chaos.2021.111065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077921004197
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2021.111065?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. Zhang, Jiancheng & Sun, Jitao, 2014. "Stability analysis of an SIS epidemic model with feedback mechanism on networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 24-32.
    2. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "An information diffusion-based model of oil futures price," Energy Economics, Elsevier, vol. 36(C), pages 518-525.
    3. Satsuma, J & Willox, R & Ramani, A & Grammaticos, B & Carstea, A.S, 2004. "Extending the SIR epidemic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 369-375.
    4. Gao, Lei & Li, Ruiqi & Shu, Panpan & Wang, Wei & Gao, Hui & Cai, Shimin, 2018. "Effects of individual popularity on information spreading in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 32-39.
    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. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2020. "Voluntary and Mandatory Social Distancing: Evidence on COVID-19 Exposure Rates from Chinese Provinces and Selected Countries," Globalization Institute Working Papers 382, Federal Reserve Bank of Dallas.
    2. Wei, Xiaodan & Zhao, Xu & Zhou, Wenshu, 2022. "Global stability of a network-based SIS epidemic model with a saturated treatment function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    3. Chen, Ning & Zhu, Xuzhen & Chen, Yanyan, 2019. "Information spreading on complex networks with general group distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 671-676.
    4. Hu, Zengyun & Teng, Zhidong & Zhang, Tailei & Zhou, Qiming & Chen, Xi, 2017. "Globally asymptotically stable analysis in a discrete time eco-epidemiological system," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 20-31.
    5. Mahajan, Shveta & Kumar, Deepak & Verma, Atul Kumar & Sharma, Natasha, 2023. "Dynamic analysis of modified SEIR epidemic model with time delay in geographical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    6. Cuñat, Alejandro & Zymek, Robert, 2022. "The (structural) gravity of epidemics," European Economic Review, Elsevier, vol. 144(C).
    7. Wang, Jinling & Jiang, Haijun & Hu, Cheng & Yu, Zhiyong & Li, Jiarong, 2021. "Stability and Hopf bifurcation analysis of multi-lingual rumor spreading model with nonlinear inhibition mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    8. Liu, Qiming & Li, Hua, 2019. "Global dynamics analysis of an SEIR epidemic model with discrete delay on complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 289-296.
    9. Liu, Jiawei & Ding, Jie, 2020. "Requesting for retweeting or donating? A research on how the fundraiser seeks help in the social charitable crowdfunding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    10. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    11. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2023. "Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 474-508, June.
    12. Liu, Lijun & Wei, Xiaodan & Zhang, Naimin, 2019. "Global stability of a network-based SIRS epidemic model with nonmonotone incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 587-599.
    13. Wang, Xingyuan & Zhao, Tianfang & Qin, Xiaomeng, 2016. "Model of epidemic control based on quarantine and message delivery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 168-178.
    14. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2021. "COVID-19 Time-Varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing," Globalization Institute Working Papers 407, Federal Reserve Bank of Dallas.
    15. Wei, Xiaodan & Xu, Gaochao & Zhou, Wenshu, 2018. "Global stability of endemic equilibrium for a SIQRS epidemic model on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 203-214.
    16. Awawdeh, Fadi & Adawi, A. & Mustafa, Z., 2009. "Solutions of the SIR models of epidemics using HAM," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 3047-3052.
    17. Narisa Zhao & Hui Li, 2020. "How can social commerce be boosted? The impact of consumer behaviors on the information dissemination mechanism in a social commerce network," Electronic Commerce Research, Springer, vol. 20(4), pages 833-856, December.
    18. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    19. Hu, Sen & Hu, Bin & Cao, Ya, 2018. "The wider, the better? The interaction between the IoT diffusion and online retailers’ decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 196-209.
    20. Huo, Jingjing & Zhao, Hongyong, 2016. "Dynamical analysis of a fractional SIR model with birth and death on heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 41-56.

    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:chsofr:v:149:y:2021:i:c:s0960077921004197. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.