Model-free inference of direct network interactions from nonlinear collective dynamics
Jose Casadiego1,2, Mor Nitzan3,4,5, Sarah Hallerberg6,7
1Chair for Network Dynamics, Institute for Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), Technical University of Dresden, 01062, Dresden, Germany. jose.casadiego@tu-dresden.de.
Nature Communications
|December 21, 2017
Summary
This study introduces a new model-independent framework to infer direct interactions in network dynamical systems from their collective dynamics. This method reliably detects network and hypernetwork interactions without prior system knowledge.
Area of Science:
- Network science
- Dynamical systems theory
- Complex systems analysis
Background:
- Understanding network interactions is crucial for system function.
- Massive data on collective nonlinear dynamics are available.
- Inferring direct interactions from dynamics is a significant challenge, often requiring prior system models.
Purpose of the Study:
- To develop a model-independent framework for inferring direct interactions from nonlinear collective dynamics.
- To provide a method applicable across diverse dynamical regimes.
- To enable the detection of both pairwise and higher-order (hypernetwork) interactions.
Main Methods:
- Developed a framework using an explicit dependency matrix.
- Employed a block-orthogonal regression algorithm.
- Applied the method to analyze nonlinear collective dynamics.
Main Results:
- The framework successfully infers direct interactions without prior system models.
- It demonstrates reliability across various dynamical regimes (steady states, periodic, chaotic).
- The approach can reveal network (two-point) and hypernetwork (e.g., three-point) interactions.
Conclusions:
- This model-independent framework advances the inference of direct interaction patterns in complex systems.
- It offers new possibilities for analyzing systems where models are unknown.
- The method has broad applicability in physical, biological, and technological networks.
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