IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v165y2022ip2s0960077922010268.html
   My bibliography  Save this article

Multiscale voter model on real networks

Author

Listed:
  • Ortiz, Elisenda
  • Serrano, M. Ángeles

Abstract

We introduce the Multiscale Voter Model (MVM) to investigate clan influence at multiple scales—family, neighborhood, political party…—in opinion formation on real complex networks. Clans, consisting of similar nodes, are constructed using a coarse-graining procedure on network embeddings that allows us to control for the length scale of interactions. We ran numerical simulations to monitor the evolution of MVM dynamics in real and synthetic networks, and identified a transition between a final stage of full consensus and one with mixed binary opinions. The transition depends on the scale of the clans and on the strength of their influence. We found that enhancing group diversity promotes consensus while strong kinship yields to metastable clusters of same opinion. The segregated domains, which signal opinion polarization, are discernible as spatial patterns in the hyperbolic embeddings of the networks. Our multiscale framework can be easily applied to other dynamical processes affected by scale and group influence.

Suggested Citation

  • Ortiz, Elisenda & Serrano, M. Ángeles, 2022. "Multiscale voter model on real networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p2:s0960077922010268
    DOI: 10.1016/j.chaos.2022.112847
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922010268
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112847?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. Guillermo Garc'ia-P'erez & Mari'an Bogu~n'a & Antoine Allard & M. 'Angeles Serrano, 2015. "The hidden hyperbolic geometry of international trade: World Trade Atlas 1870-2013," Papers 1512.02233, arXiv.org, revised May 2016.
    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. Dai, Liang & Derudder, Ben & Liu, Xingjian, 2018. "Transport network backbone extraction: A comparison of techniques," Journal of Transport Geography, Elsevier, vol. 69(C), pages 271-281.
    2. Antoine Allard & M Ángeles Serrano, 2020. "Navigable maps of structural brain networks across species," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-20, February.
    3. Béatrice Dedinger & Paul Girard, 2018. "RICardo World Trade Web, 1834-1938," Post-Print hal-03619636, HAL.
    4. Zádor, Zsófia & Zhu, Zhen & Smith, Matthew & Gorgoni, Sara, 2022. "A weighted and normalized Gould–Fernandez brokerage measure," Greenwich Papers in Political Economy 37794, University of Greenwich, Greenwich Political Economy Research Centre.
    5. Béatrice Dedinger & Paul Girard, 2018. "RICardo World Trade Web, 1834-1938," SciencePo Working papers Main hal-03619636, HAL.
    6. Junya Wang & Yi-Jiao Zhang & Cong Xu & Jiaze Li & Jiachen Sun & Jiarong Xie & Ling Feng & Tianshou Zhou & Yanqing Hu, 2024. "Reconstructing the evolution history of networked complex systems," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. repec:spo:wpmain:info:hdl:2441/6h7io1v56e8k4qtht2cuvjcfa5 is not listed on IDEAS
    8. Inna Čábelková & Luboš Smutka & Svitlana Rotterova & Olesya Zhytna & Vít Kluger & David Mareš, 2022. "The Sustainability of International Trade: The Impact of Ongoing Military Conflicts, Infrastructure, Common Language, and Economic Wellbeing in Post-Soviet Region," Sustainability, MDPI, vol. 14(17), pages 1-14, August.
    9. repec:hal:spmain:info:hdl:2441/6h7io1v56e8k4qtht2cuvjcfa5 is not listed on IDEAS
    10. Pawanesh & Charu Sharma & Niteesh Sahni, 2024. "Explaining Indian Stock Market through Geometry of Scale free Networks," Papers 2404.04710, arXiv.org, revised Oct 2024.
    11. Robert Jankowski & Antoine Allard & Marián Boguñá & M. Ángeles Serrano, 2023. "The D-Mercator method for the multidimensional hyperbolic embedding of real networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    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:165:y:2022:i:p2:s0960077922010268. 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.