IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v511y2018icp272-279.html
   My bibliography  Save this article

A general social contagion dynamic in interconnected lattices

Author

Listed:
  • Shu, Panpan
  • Wang, Wei
  • Eugene Stanley, H.
  • Braunstein, Lidia A.

Abstract

Research on dynamical processes in interconnected spatial networks has expanded in recent years, but there has been little focus on social contagions. Using a general social contagion model, we numerically study how an interconnected spatial system composed of two interconnected planar lattices influences social contagion dynamics. When information is transmitted and allows for a probability of behavior adoption, strongly interconnected lattices stimulate the contagion process and significantly increase the final density of adopted individuals. We perform a finite-size analysis and confirm that the dependency of prevalence on the transmission rate is continuous regardless of the adoption probability. The prevalence grows discontinuously with the adoption probability even when the transmission rate is low. Although a high transmission rate or a high adoption probability increases the final adopted density in weak interconnected lattices, the prevalence always grows continuously in these networks. These findings help us understand social contagion dynamics in interconnected lattices.

Suggested Citation

  • Shu, Panpan & Wang, Wei & Eugene Stanley, H. & Braunstein, Lidia A., 2018. "A general social contagion dynamic in interconnected lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 272-279.
    • Handle: RePEc:eee:phsmap:v:511:y:2018:i:c:p:272-279
    DOI: 10.1016/j.physa.2018.07.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711830921X
    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.2018.07.049?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. Jon M. Kleinberg, 2000. "Navigation in a small world," Nature, Nature, vol. 406(6798), pages 845-845, August.
    2. Filippo Radicchi & Claudio Castellano, 2015. "Breaking of the site-bond percolation universality in networks," Nature Communications, Nature, vol. 6(1), pages 1-7, December.
    3. Lin Wang & Joseph T. Wu, 2018. "Characterizing the dynamics underlying global spread of epidemics," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    4. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    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. Alan Gerber & Mitchell Hoffman & John Morgan & Collin Raymond, 2020. "One in a Million: Field Experiments on Perceived Closeness of the Election and Voter Turnout," American Economic Journal: Applied Economics, American Economic Association, vol. 12(3), pages 287-325, July.
    2. Ruyi Ge & Juan Feng & Bin Gu, 2016. "Borrower’s default and self-disclosure of social media information in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-6, December.
    3. Jiang, Lincheng & Zhao, Xiang & Ge, Bin & Xiao, Weidong & Ruan, Yirun, 2019. "An efficient algorithm for mining a set of influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 58-65.
    4. Andrea Avena-Koenigsberger & Xiaoran Yan & Artemy Kolchinsky & Martijn P van den Heuvel & Patric Hagmann & Olaf Sporns, 2019. "A spectrum of routing strategies for brain networks," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    5. Yann Algan & Quoc-Anh Do & Nicolò Dalvit & Alexis Le Chapelain & Yves Zenou, 2015. "How Social Networks Shape Our Beliefs: A Natural Experiment among Future French Politicians," Working Papers hal-03459820, HAL.
    6. Daniele Barchiesi & Helen Susannah Moat & Christian Alis & Steven Bishop & Tobias Preis, 2015. "Quantifying International Travel Flows Using Flickr," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-8, July.
    7. Julian Freitag & Anna Kerkhof & Johannes Münster, 2021. "Selective sharing of news items and the political position of news outlets," ECONtribute Discussion Papers Series 056, University of Bonn and University of Cologne, Germany.
    8. Donati, Dante, 2023. "Mobile Internet access and political outcomes: Evidence from South Africa," Journal of Development Economics, Elsevier, vol. 162(C).
    9. Yuho Chung & Yiwei Li & Jianmin Jia, 2021. "Exploring embeddedness, centrality, and social influence on backer behavior: the role of backer networks in crowdfunding," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 925-946, September.
    10. Liberini, Federica & Redoano, Michela & Russo, Antonio & Cuevas, Angel & Cuevas, Ruben, 2018. "Politics in the Facebook Era Evidence from the 2016 US Presidential Elections," CAGE Online Working Paper Series 389, Competitive Advantage in the Global Economy (CAGE).
    11. Peter Biddle & Paul England & Marcus Peinado & Bryan Willman, 2003. "The Darknet and the Future of Content Distribution," Levine's Working Paper Archive 618897000000000636, David K. Levine.
    12. 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.
    13. Joost Berkhout & Bernd F. Heidergott, 2019. "Analysis of Markov Influence Graphs," Operations Research, INFORMS, vol. 67(3), pages 892-904, May.
    14. DiTraglia, Francis J. & García-Jimeno, Camilo & O’Keeffe-O’Donovan, Rossa & Sánchez-Becerra, Alejandro, 2023. "Identifying causal effects in experiments with spillovers and non-compliance," Journal of Econometrics, Elsevier, vol. 235(2), pages 1589-1624.
    15. Kondor, Dániel & Mátray, Péter & Csabai, István & Vattay, Gábor, 2013. "Measuring the dimension of partially embedded networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4160-4171.
    16. Khalid Bakhshaliyev & Mehmet Hadi Gunes, 2020. "Generation of 2-mode scale-free graphs for link-level internet topology modeling," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.
    17. Shota Saito & Yoshito Hirata & Kazutoshi Sasahara & Hideyuki Suzuki, 2015. "Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    18. Komatsu, Hidenori & Nishio, Ken-ichiro, 2015. "An experimental study on motivational change for electricity conservation by normative messages," Applied Energy, Elsevier, vol. 158(C), pages 35-43.
    19. Nicolas Jonard & R. Cowan & B. Sanditov, 2009. "Fits and Misfits : Technological Matching and R & D Networks," DEM Discussion Paper Series 09-12, Department of Economics at the University of Luxembourg.
    20. Xueli Wang & Moqin Zhou & Jinzhu Jia & Zhi Geng & Gexin Xiao, 2018. "A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases," IJERPH, MDPI, vol. 15(8), pages 1-13, August.

    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:511:y:2018:i:c:p:272-279. 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.