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Dynamics of cascades on burstiness-controlled temporal networks

Author

Listed:
  • Samuel Unicomb

    (Université de Lyon, ENS de Lyon, INRIA, CNRS)

  • Gerardo Iñiguez

    (Central European University
    Aalto University School of Science
    Universidad Nacional Autonóma de México)

  • James P. Gleeson

    (University of Limerick)

  • Márton Karsai

    (Université de Lyon, ENS de Lyon, INRIA, CNRS
    Central European University)

Abstract

Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. Here we develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics.

Suggested Citation

  • Samuel Unicomb & Gerardo Iñiguez & James P. Gleeson & Márton Karsai, 2021. "Dynamics of cascades on burstiness-controlled temporal networks," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20398-4
    DOI: 10.1038/s41467-020-20398-4
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    Cited by:

    1. Anzhi Sheng & Qi Su & Aming Li & Long Wang & Joshua B. Plotkin, 2023. "Constructing temporal networks with bursty activity patterns," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Kobayashi, Teruyoshi & Ogisu, Yoshitaka & Onaga, Tomokatsu, 2023. "Unstable diffusion in social networks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    3. Yao Meng & Sean P. Cornelius & Yang-Yu Liu & Aming Li, 2024. "Dynamics of collective cooperation under personalised strategy updates," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. 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.
    5. Agnieszka Geras & Grzegorz Siudem & Marek Gagolewski, 2022. "Time to vote: Temporal clustering of user activity on Stack Overflow," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1681-1691, December.

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