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Characteristic scales and adaptation in higher-order contagions

Author

Listed:
  • Giulio Burgio

    (Universitat Rovira i Virgili
    University of Vermont)

  • Guillaume St-Onge

    (the Roux Institute at Northeastern University
    the Roux Institute at Northeastern University)

  • Laurent Hébert-Dufresne

    (University of Vermont
    University of Vermont)

Abstract

People organize in groups and contagions spread across them. A simple stochastic process, yet complex to model due to dynamical correlations within and between groups. Moreover, groups can evolve if agents join or leave in response to contagions. To address the lack of analytical models that account for dynamical correlations and adaptation in groups, we introduce the method of generalized approximate master equations. We first analyze how nonlinear contagions differ when driven by group-level or individual-level dynamics. We then study the characteristic levels of group activity that best describe the stochastic process and that optimize agents’ ability to adapt to it. Naturally lending itself to study adaptive hypergraphs, our method reveals how group structure unlocks new dynamical regimes and enables distinct suitable adaptation strategies. Our approach offers a highly accurate model of binary-state dynamics on hypergraphs, advances our understanding of contagion processes, and opens the study of adaptive group-structured systems.

Suggested Citation

  • Giulio Burgio & Guillaume St-Onge & Laurent Hébert-Dufresne, 2025. "Characteristic scales and adaptation in higher-order contagions," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59777-0
    DOI: 10.1038/s41467-025-59777-0
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    References listed on IDEAS

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    2. 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.
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