Reverse engineering of complex dynamical networks in the presence of time-delayed interactions based on noisy time series

TL;DR

This paper presents a novel method to reconstruct complex oscillator network topologies and estimate time delays from noisy time series data, advancing the understanding of dynamical networks.

Contribution

The authors develop an analytic approach that uses the dynamical correlation matrix to infer both network structure and time delays from noisy observations.

Findings

Method accurately reconstructs network topology from noisy data

Simultaneously estimates time delay and network structure

Validated extensively with numerical simulations

Abstract

Reverse engineering of complex dynamical networks is important for a variety of fields where uncovering the full topology of unknown networks and estimating parameters characterizing the network structure and dynamical processes are of interest. We consider complex oscillator networks with time-delayed interactions in a noisy environment, and develop an effective method to infer the full topology of the network and evaluate the amount of time delay based solely on noise- contaminated time series. In particular, we develop an analytic theory establishing that the dynamical correlation matrix, which can be constructed purely from time series, can be manipulated to yield both the network topology and the amount of time delay simultaneously. Extensive numerical support is provided to validate the method. While our method provides a viable solution to the network inverse problem, significant difficulties, limitations, and challenges still remain, and these are discussed thoroughly.