Detecting Directed Interactions of Networks by Random Variable Resetting

TL;DR

This paper introduces a new method for identifying directed interactions in complex dynamic networks by using random variable resetting and averaging, effective even with nonlinear dynamics, hidden variables, and noise.

Contribution

The paper presents a novel approach for detecting directed network interactions through random variable resetting, applicable to diverse nonlinear systems with hidden variables and noise.

Findings

Method accurately infers pairwise coupling functions.

Validated on numerical simulations with complex dynamics.

Effective under conditions of noise and hidden variables.

Abstract

We propose a novel method of detecting directed interactions of a general dynamic network from measured data. By repeating random state variable resetting of a target node and appropriately averaging over the measurable data, the pairwise coupling function between the target and the response nodes can be inferred. This method is applicable to a wide class of networks with nonlinear dynamics, hidden variables and strong noise. The numerical results have fully verified the validity of the theoretical derivation.