Inferring interactions of time-delayed dynamic networks by random state variable resetting

doi:10.1088/1674-1056/ac1e12

Abstract Time delays exist widely in real systems, and time-delayed interactions can result in abundant dynamic behaviors and functions in dynamic networks. Inferring the time delays and interactions is challenging due to systematic nonlinearity, noises, a lack of information, and so on. Recently, Shi et al. proposed a random state variable resetting method to detect the interactions in a continuous-time dynamic network. By arbitrarily resetting the state variable of a driving node, the equivalent coupling functions of the driving node to any response node in the network can be reconstructed. In this paper, we introduce this method in time-delayed dynamic networks. To infer actual time delays, the nearest neighbor correlation (NNC) function for a given time delay is defined. The significant increments of NNC originate from the delayed effect. Based on the increments, the time delays can be reconstructed and the reconstruction errors depend on the sampling time interval. After time delays are accurately identified, the equivalent coupling functions can also be reconstructed. The numerical results have fully verified the validity of the theoretical analysis.

Keywords: time delays network reconstruction random state variable resetting

Received: 19 April 2021
Revised: 20 July 2021
Accepted manuscript online: 17 August 2021

PACS:	05.45.-a	(Nonlinear dynamics and chaos)
	64.60.aq	(Networks)
	89.75.Fb	(Structures and organization in complex systems)

Corresponding Authors: Shihong Wang
E-mail: shwang@bupt.edu.cn

Cite this article:

Changbao Deng(邓长宝), Weinuo Jiang(蒋未诺), and Shihong Wang(王世红) Inferring interactions of time-delayed dynamic networks by random state variable resetting 2022 Chin. Phys. B 31 030502

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Inferring interactions of time-delayed dynamic networks by random state variable resetting

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