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Model-free inference of direct network interactions from nonlinear collective dynamics

Author

Listed:
  • Jose Casadiego

    (Technical University of Dresden
    Max Planck Institute for Dynamics and Self-Organization (MPIDS))

  • Mor Nitzan

    (The Hebrew University
    The Hebrew University
    The Hebrew University)

  • Sarah Hallerberg

    (Max Planck Institute for Dynamics and Self-Organization (MPIDS)
    Hamburg University of Applied Sciences)

  • Marc Timme

    (Technical University of Dresden
    Max Planck Institute for Dynamics and Self-Organization (MPIDS)
    Bernstein Center for Computational Neuroscience (BCCN)
    Technical University of Darmstadt)

Abstract

The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known.

Suggested Citation

  • Jose Casadiego & Mor Nitzan & Sarah Hallerberg & Marc Timme, 2017. "Model-free inference of direct network interactions from nonlinear collective dynamics," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-02288-4
    DOI: 10.1038/s41467-017-02288-4
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    Cited by:

    1. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Sysoeva, Marina V. & Sysoev, Ilya V. & Prokhorov, Mikhail D. & Ponomarenko, Vladimir I. & Bezruchko, Boris P., 2021. "Reconstruction of coupling structure in network of neuron-like oscillators based on a phase-locked loop," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Li, Zhongyang & Lu, Fei & Maggioni, Mauro & Tang, Sui & Zhang, Cheng, 2021. "On the identifiability of interaction functions in systems of interacting particles," Stochastic Processes and their Applications, Elsevier, vol. 132(C), pages 135-163.
    4. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. Atte Aalto & Lauri Viitasaari & Pauliina Ilmonen & Laurent Mombaerts & Jorge Gonçalves, 2020. "Gene regulatory network inference from sparsely sampled noisy data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.

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