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

doi:10.1088/1674-1056/ac1e12

中国物理B ›› 2022, Vol. 31 ›› Issue (3): 30502-030502.doi: 10.1088/1674-1056/ac1e12

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

Changbao Deng(邓长宝), Weinuo Jiang(蒋未诺), and Shihong Wang(王世红)^†

School of Sciences, Beijing University of Posts and Telecommunications, Beijing 100876, China

收稿日期:2021-04-19 修回日期:2021-07-20 接受日期:2021-08-17 出版日期:2022-02-22 发布日期:2022-02-14
通讯作者: Shihong Wang E-mail:shwang@bupt.edu.cn

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

Changbao Deng(邓长宝), Weinuo Jiang(蒋未诺), and Shihong Wang(王世红)^†

School of Sciences, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2021-04-19 Revised:2021-07-20 Accepted:2021-08-17 Online:2022-02-22 Published:2022-02-14
Contact: Shihong Wang E-mail:shwang@bupt.edu.cn

摘要/Abstract

摘要： 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.

关键词: time delays, network reconstruction, random state variable resetting

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.

Key words: time delays, network reconstruction, random state variable resetting

中图分类号: (Nonlinear dynamics and chaos)

05.45.-a

64.60.aq (Networks) 89.75.Fb (Structures and organization in complex systems)

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[J]. 中国物理B, 2022, 31(3): 30502-030502.

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[J]. Chin. Phys. B, 2022, 31(3): 30502-030502.

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

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

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