Characteristic scales and adaptation in higher-order contagions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916749162577920 |
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| author | Burgio, Giulio St-Onge, Guillaume Hébert-Dufresne, Laurent |
| author_facet | Burgio, Giulio St-Onge, Guillaume Hébert-Dufresne, Laurent |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_14930 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Characteristic scales and adaptation in higher-order contagions Burgio, Giulio St-Onge, Guillaume Hébert-Dufresne, Laurent Physics and Society Adaptation and Self-Organizing Systems 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. |
| title | Characteristic scales and adaptation in higher-order contagions |
| topic | Physics and Society Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2505.14930 |