Revealing networks from dynamics: an introduction

TL;DR

This paper reviews methods to infer the underlying structure of complex networks from their observed dynamics, with applications across physics, biology, and engineering, highlighting recent progress in the field.

Contribution

It provides an overview of recent advances in inferring structural and functional connectivity from dynamic data in various complex systems.

Findings

Progress in inferring structural connectivity from dynamics

Applications across multiple scientific fields

Discussion of methods for effective connectivity inference

Abstract

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.