Network topology reconstructed from derivative-variable correlations

TL;DR

This paper introduces a novel method for reconstructing network topology from dynamical time series data using derivative-variable correlations, which is optimized for accuracy and robustness.

Contribution

The paper presents a new approach to network reconstruction that utilizes derivative-variable correlations and provides a way to optimize and estimate the precision of the reconstruction.

Findings

Effective reconstruction from short time series

Robust to model and observation errors

Provides a reliable precision estimate

Abstract

A method of network reconstruction from the dynamical time series is introduced, relying on the concept of derivative-variable correlation. Using a tunable observable as a parameter, the reconstruction of any network with known interaction functions is formulated via simple matrix equation. We suggest a procedure aimed at optimizing the reconstruction from the time series of length comparable to the characteristic dynamical time scale. Our method also provides a reliable precision estimate. We illustrate the method's implementation via elementary dynamical models, and demonstrate its robustness to both model and observation errors.