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

Jose Casadiego*, Mor Nitzan, Sarah Hallerberg, Marc Timme

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


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.

Original languageAmerican English
Article number2192
JournalNature Communications
Issue number1
StatePublished - 1 Dec 2017

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© 2017 The Author(s).


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