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

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 regim

SUBMITTER: Casadiego J 

PROVIDER: S-EPMC5736722 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature