IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v567y2021ics0378437120309754.html
   My bibliography  Save this article

Belief model of complex contagions on random networks

Author

Listed:
  • Li, Yang
  • Sun, Hao
  • Xiong, Wanda
  • Xu, Genjiu

Abstract

We proposed a belief model on random networks to explore the process of opinion dissemination, focusing on 3 significant factors: the inherent beliefs of individuals, the heterogeneity of individual persuasion ability and the dilution effect of neighbor size on neighbors’ persuasion. By mean-field approximation approach, the theoretical final fraction of active agents is determined, which agrees well with simulation results in most situations but diverges around critical condition. This divergence is demonstrated to be caused by the heterogeneity of properties of the initial active node and inevitable for any mean solution. As an alternative to predict and control the contagion, we proposed two strategies for selecting the initial active node based on its properties. Their efficiencies are discussed on different networks by simulations.

Suggested Citation

  • Li, Yang & Sun, Hao & Xiong, Wanda & Xu, Genjiu, 2021. "Belief model of complex contagions on random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    • Handle: RePEc:eee:phsmap:v:567:y:2021:i:c:s0378437120309754
    DOI: 10.1016/j.physa.2020.125677
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120309754
    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.2020.125677?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. Teruyoshi Kobayashi, 2015. "Trend-driven information cascades on random networks," Discussion Papers 1529, Graduate School of Economics, Kobe University.
    2. Gai, Prasanna & Kapadia, Sujit, 2010. "Contagion in financial networks," Bank of England working papers 383, Bank of England.
    3. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    4. Ioannidis, Evangelos & Varsakelis, Nikos & Antoniou, Ioannis, 2017. "False Beliefs in Unreliable Knowledge Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 275-295.
    5. Xuzhen Zhu & Jinming Ma & Xin Su & Hui Tian & Wei Wang & Shimin Cai, 2019. "Information Spreading on Weighted Multiplex Social Network," Complexity, Hindawi, vol. 2019, pages 1-15, November.
    6. Chen, Ling-Jiao & Chen, Xiao-Long & Cai, Meng & Wang, Wei, 2018. "Complex contagions with social reinforcement from different layers and neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 516-525.
    7. Zhu, Shu-Shan & Zhu, Xu-Zhen & Wang, Jian-Qun & Zhang, Zeng-Ping & Wang, Wei, 2019. "Social contagions on multiplex networks with heterogeneous population," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 105-113.
    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. Fabio Caccioli & Paolo Barucca & Teruyoshi Kobayashi, 2018. "Network models of financial systemic risk: a review," Journal of Computational Social Science, Springer, vol. 1(1), pages 81-114, January.
    2. Georg, Co-Pierre, 2014. "Contagious herding and endogenous network formation in financial networks," Working Paper Series 1700, European Central Bank.
    3. Stiglitz Joseph E., 2010. "Contagion, Liberalization, and the Optimal Structure of Globalization," Journal of Globalization and Development, De Gruyter, vol. 1(2), pages 1-47, December.
    4. Patrick Bayer & Kyle Mangum & James W. Roberts, 2021. "Speculative Fever: Investor Contagion in the Housing Bubble," American Economic Review, American Economic Association, vol. 111(2), pages 609-651, February.
    5. David Aikman & Mirta Galesic & Gerd Gigerenzer & Sujit Kapadia & Konstantinos Katsikopoulos & Amit Kothiyal & Emma Murphy & Tobias Neumann, 2021. "Taking uncertainty seriously: simplicity versus complexity in financial regulation [Uncertainty in macroeconomic policy-making: art or science?]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(2), pages 317-345.
    6. Teruyoshi Kobayashi & Tomokatsu Onaga, 2023. "Dynamics of diffusion on monoplex and multiplex networks: a message-passing approach," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 76(1), pages 251-287, July.
    7. Aikman, David & Galesic, Mirta & Gigerenzer, Gerd & Kapadia, Sujit & Katsikopoulos, Konstantinos & Kothiyal, Amit & Murphy, Emma & Neumann, Tobias, 2014. "Financial Stability Paper No 28: Taking uncertainty seriously - simplicity versus complexity in financial regulation," Bank of England Financial Stability Papers 28, Bank of England.
    8. Chang, Eric C. & Cheng, Joseph W. & Khorana, Ajay, 2000. "An examination of herd behavior in equity markets: An international perspective," Journal of Banking & Finance, Elsevier, vol. 24(10), pages 1651-1679, October.
    9. Gu, Chen & Guo, Xu & Zhang, Chengping, 2022. "Analyst target price revisions and institutional herding," International Review of Financial Analysis, Elsevier, vol. 82(C).
    10. Ferdinand Thies & Sören Wallbach & Michael Wessel & Markus Besler & Alexander Benlian, 2022. "Initial coin offerings and the cryptocurrency hype - the moderating role of exogenous and endogenous signals," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1691-1705, September.
    11. Ruomeng Cui & Dennis J. Zhang & Achal Bassamboo, 2019. "Learning from Inventory Availability Information: Evidence from Field Experiments on Amazon," Management Science, INFORMS, vol. 65(3), pages 1216-1235, March.
    12. Nils Detering & Thilo Meyer-Brandis & Konstantinos Panagiotou & Daniel Ritter, 2018. "Financial Contagion in a Generalized Stochastic Block Model," Papers 1803.08169, arXiv.org, revised Dec 2019.
    13. Stéphan Marette, 2017. "Jill E. Hobbs, Stavroula Malla, Eric K. Sogah and May T. Yeung, 2014, Regulating Health Foods. Policy Challenges and Consumer Conundrums," Review of Agricultural, Food and Environmental Studies, Springer, vol. 98(1), pages 93-94, July.
    14. Jonas Hedlund & Carlos Oyarzun, 2018. "Imitation in heterogeneous populations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(4), pages 937-973, June.
    15. Cao, Melanie & Shi, Shouyong, 2006. "Signaling in the Internet craze of initial public offerings," Journal of Corporate Finance, Elsevier, vol. 12(4), pages 818-833, September.
    16. Martin Andersson & Johan P. Larsson, 2022. "Historical local industry structure, voting patterns and the long-run entrepreneurial character of regions: Swedish examples," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(3), pages 611-631, December.
    17. Wei He & Qian Wang, 2020. "The peer effect of corporate financial decisions around split share structure reform in China," Review of Financial Economics, John Wiley & Sons, vol. 38(3), pages 474-493, July.
    18. Champagne, Claudia, 2014. "The international syndicated loan market network: An “unholy trinity”?," Global Finance Journal, Elsevier, vol. 25(2), pages 148-168.
    19. Ben Klemens, 2013. "A Peer-based Model of Fat-tailed Outcomes," Papers 1304.0718, arXiv.org.
    20. Kraemer, Carlo & Noth, Markus & Weber, Martin, 2006. "Information aggregation with costly information and random ordering: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 59(3), pages 423-432, March.

    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:567:y:2021:i:c:s0378437120309754. 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.