ArticleScience advances2017
Revealing physical interaction networks from statistics of collective dynamics.
Article in Science advances, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- AIPI: Network Status Identification on Multi-Protocol Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2025Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Statistical inference links data and theory in network science.Nature communications · 2022Review
- Predicting network dynamics without requiring the knowledge of the interaction graph.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Network structure from a characterization of interactions in complex systems.Scientific reports · 2022Article
- CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information.Nature biotechnology · 2022Article
- The brainstem connectome database.Scientific data · 2022Article
- Modes of information flow in collective cohesion.Science advances · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- Physically-interpretable classification of biological network dynamics for complex collective motions.Scientific reports · 2020Article
- The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges.Frontiers in physiology · 2020Article
- A practical method for estimating coupling functions in complex dynamical systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- Network Reconstruction and Community Detection from Dynamics.Physical review letters · 2019Article
- Optimal information networks: Application for data-driven integrated health in populations.Science advances · 2018Article
- Model-free inference of direct network interactions from nonlinear collective dynamics.Nature communications · 2017Article
- Transition to reconstructibility in weakly coupled networks.PloS one · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Revealing physical interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Current reconstruction methods require access to a system's model or dynamical data at a level of detail often not available. We exploit changes in invariant measures, in particular distributions of sampled states of the system in response to driving signals, and use compressed sensing to reveal physical interaction networks. Dynamical observations following driving suffice to infer physical connectivity even if they are temporally disordered, are acquired at large sampling intervals, and stem from different experiments. Testing various nonlinear dynamic processes emerging on artificial and real network topologies indicates high reconstruction quality for existence as well as type of interactions. These results advance our ability to reveal physical interaction networks in complex synthetic and natural systems.
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