ArticleNature communications2017
Model-free inference of direct network interactions from nonlinear collective dynamics.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
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- The inferred functional connectome underlying circadian synchronization in the mouse suprachiasmatic nucleus.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- Dynamical systems theory as an organizing principle for single-cell biology.NPJ systems biology and applications · 2025Review
- Inferring networks of chemical reactions by curvature analysis of kinetic trajectories.Physical chemistry chemical physics : PCCP · 2025Article
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- The Functional Connectome Mediating Circadian Synchrony in the Suprachiasmatic Nucleus.bioRxiv : the preprint server for biology · 2024Article
- Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.Entropy (Basel, Switzerland) · 2024Article
- Spatio-temporal breather dynamics in microcomb soliton crystals.Light, science & applications · 2024Article
- Learning interpretable dynamics of stochastic complex systems from experimental data.Nature communications · 2024Article
- Network properties determine neural network performance.Nature communications · 2024Article
- Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data.NPJ systems biology and applications · 2024Article
- The Experimentalist's Guide to Machine Learning for Small Molecule Design.ACS applied bio materials · 2024Review
- Network inference from short, noisy, low time-resolution, partial measurements: Application toProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Data-Efficient Inference of Nonlinear Oscillator Networks.IFAC-PapersOnLine · 2023Article
- Neural Information Squeezer for Causal Emergence.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.