Detecting interactions in discrete-time dynamics by random variable resetting

Detecting the interactions in networks helps us to understand the collective behaviors of complex systems. However, doing so is challenging due to systemic noise, nonlinearity, and a lack of information. Very few researchers have attempted to reconstruct discrete-time dynamic networks. Recently, Shi et al. proposed resetting a random state variable to infer the interactions in a continuous-time dynamic network. In this paper, we introduce a random resetting method for discrete-time dynamic networks. The statistical characteristics of the method are investigated and verified with numerical simulations. In addition, this reconstruction method was evaluated for limited data and weak coupling and within multiple-attractor systems.

Automated summary

This paper introduces a random resetting method for discrete-time dynamic networks, investigates the statistical characteristics of the method with numerical simulations, and evaluates the method for limited data, weak coupling, and multiple-attractor systems.