Revealing networks from dynamics: an introduction

By Marc Timme, Jose Casadiego. 2014

188 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/77298fa7c6dd021d3b50c7163f5e76fbf354fc5c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Molecular Biology

Citations in the corpus

What are higher-order networks? (2021, background): “, modularity [34, 180]) influence a dynamical process on a network? On the other hand, how can the network structure and interactions be inferred from measurements of the dynamics x(t)? This last problem is commonly known as inference of network dynamical systems [275].”
Control Principles of Complex Networks (2015, background): “There are three principally different NR approaches, which assume various levels of a priori knowledge about the system (Timme and Casadiego, 2014).”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, background): “Numerical tests [34] showed that the number M of the required experimental realizations can be substantially smaller than N to yield reasonable reconstruction results.”