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Hypergraph reconstruction from dynamics

Author

Listed:
  • Robin Delabays

    (University of Applied Sciences of Western Switzerland HES-SO)

  • Giulia De Pasquale

    (Eindhoven University of Technology)

  • Florian Dörfler

    (ETH Zürich)

  • Yuanzhao Zhang

    (Santa Fe Institute)

Abstract

A plethora of methods have been developed in the past two decades to infer the underlying network structure of an interconnected system from its collective dynamics. However, methods capable of inferring nonpairwise interactions are only starting to appear. Here, we develop an inference algorithm based on sparse identification of nonlinear dynamics (SINDy) to reconstruct hypergraphs and simplicial complexes from time-series data. Our model-free method does not require information about node dynamics or coupling functions, making it applicable to complex systems that do not have a reliable mathematical description. We first benchmark the new method on synthetic data generated from Kuramoto and Lorenz dynamics. We then use it to infer the effective connectivity in the brain from resting-state EEG data, which reveals significant contributions from non-pairwise interactions in shaping the macroscopic brain dynamics.

Suggested Citation

  • Robin Delabays & Giulia De Pasquale & Florian Dörfler & Yuanzhao Zhang, 2025. "Hypergraph reconstruction from dynamics," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57664-2
    DOI: 10.1038/s41467-025-57664-2
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    References listed on IDEAS

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    1. Jacopo Grilli & György Barabás & Matthew J. Michalska-Smith & Stefano Allesina, 2017. "Higher-order interactions stabilize dynamics in competitive network models," Nature, Nature, vol. 548(7666), pages 210-213, August.
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    3. Yuanzhao Zhang & Maxime Lucas & Federico Battiston, 2023. "Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    4. Iacopo Iacopini & Giovanni Petri & Alain Barrat & Vito Latora, 2019. "Simplicial models of social contagion," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    5. Hanlin Sun & Filippo Radicchi & Jürgen Kurths & Ginestra Bianconi, 2023. "The dynamic nature of percolation on networks with triadic interactions," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
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