Network synchronizability enhancement via adding antagonistic interactions

Abstract We discover a “less-is-more” effect that adding local antagonistic interactions (negative edge weights) can enhance the overall synchronizability of a dynamical network system. To explain this seemingly counterintuitive phenomenon, a condition is established to identify those edges the weight reduction of which improves the synchronizability index of the underlying network. We further reveal that this condition can be interpreted from the perspective of resistance distance and network community structure. The obtained result is also verified via numerical experiments on a 14-node network and a 118-node network. Our finding brings new thoughts and inspirations to the future directions of optimal network design problems.

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Publication Details

Journal
Autonomous Intelligent Systems
Published
2024-12-31
DOI
https://doi.org/10.1007/s43684-024-00086-6
Citations
1
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.35

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Network synchronizability enhancement via adding antagonistic interactions

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Neural Networks Stability and Synchronization
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article

Network synchronizability enhancement via adding antagonistic interactions

Yue Song, Dingmei Wang, Xiaoqin Liu, Mengqi Xue, Pengfei Gao
article en
1 citations

Abstract

Abstract We discover a “less-is-more” effect that adding local antagonistic interactions (negative edge weights) can enhance the overall synchronizability of a dynamical network system. To explain this seemingly counterintuitive phenomenon, a condition is established to identify those edges the weight reduction of which improves the synchronizability index of the underlying network. We further reveal that this condition can be interpreted from the perspective of resistance distance and network community structure. The obtained result is also verified via numerical experiments on a 14-node network and a 118-node network. Our finding brings new thoughts and inspirations to the future directions of optimal network design problems.

Autonomous Intelligent SystemsVol. 4(1)
Tongji University (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 35%
Neural Networks Stability and Synchronization
0.35
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