ArticleProceedings of the National Academy of Sciences of the United States of America2018
Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- NIPS: Network Inference with Partial State measurements using forced-delay embedding.PNAS nexus · 2026Article
- Feedback Control of Coupled Nonlinear Oscillators with Uncertain Parameters.Systems & control letters · 2025Article
- Inferring networks of chemical reactions by curvature analysis of kinetic trajectories.Physical chemistry chemical physics : PCCP · 2025Article
- Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.Entropy (Basel, Switzerland) · 2024Article
- Synchronization, clustering, and weak chimeras in a densely coupled transcription-based oscillator model for split circadian rhythms.Chaos (Woodbury, N.Y.) · 2023Article
- On quantification and maximization of information transfer in network dynamical systems.Scientific reports · 2023Article
- Data-Driven Control of Neuronal Networks with Population-Level Measurement.Research square · 2023Article
- Article
- Data-Efficient Inference of Nonlinear Oscillator Networks.IFAC-PapersOnLine · 2023Article
- Statistical inference links data and theory in network science.Nature communications · 2022Review
- Deciphering clock cell network morphology within the biological master clock, suprachiasmatic nucleus: From the perspective of circadian wave dynamics.PLoS computational biology · 2022Article
- Article
- Computational Approaches and Tools as Applied to the Study of Rhythms and Chaos in Biology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Social synchronization of circadian rhythms with a focus on honeybees.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Remarkable Sensitivity of Young Honey Bee Workers to Multiple Non-photic, Non-thermal, Forager Cues That Synchronize Their Daily Activity Rhythms.Frontiers in physiology · 2021Article
- Parallel residual projection: a new paradigm for solving linear inverse problems.Scientific reports · 2020Article
- Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.Scientific reports · 2020Article
- Anti-phase collective synchronization with intrinsic in-phase coupling of two groups of electrochemical oscillators.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- The organization of the basal ganglia functional connectivity network is non-linear in Parkinson's disease.NeuroImage. Clinical · 2019Article
- Uncertainty Quantification of Network Inference with Data Sufficiency.IEEE transactions on network science and engineeringArticle
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Authors and funding
9 authors.
Funding
Abstract
Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.
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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.