Reconstructing Links in Directed Networks from Noisy Dynamics

TL;DR

This paper introduces a general method for reconstructing directed network links from noisy dynamic measurements, leveraging noise-induced relations between network structure and covariance functions, with the ability to also estimate link weights under certain conditions.

Contribution

The authors propose a novel approach that utilizes noise-induced covariance relations to reconstruct directed network links and weights from measurement data.

Findings

Effective reconstruction of network links from noisy data.

Ability to estimate link weights for specific coupling functions.

Applicable to a broad class of directed networks.

Abstract

In this Letter, we address the longstanding challenge of how to reconstruct links in directed networks from measurements, and present a general method that makes use of a noise-induced relation between network structure and both the time-lagged covariance of measurements taken at two different times and the covariance of measurements taken at the same time. For coupling functions that have additional properties, we can further reconstruct the weights of the links.