Pinning synchronization of a complex network: Nodes, edges and higher-order edges

Abstract In recent years, the interdisciplinary study of complex networks has become increasingly important in fields ranging from biology and physics to sociology and mathematics. This paper focuses on pinning control, an approach essential for achieving coordinated behavior in dynamic networks. We explore recent advancements in pinning control strategies, explaining theoretical frameworks and simulation techniques. Additionally, we discuss the significance of certain structures within networks across different orders. Finally, we conclude with a summary of key insights and propose our outlook on future research.

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Publication Details

Journal
Europhysics Letters (EPL)
Published
2024-09-01
DOI
https://doi.org/10.1209/0295-5075/ad76d6
Citations
3
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
1.05

Funders

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article

Pinning synchronization of a complex network: Nodes, edges and higher-order edges

Jinhu Lü, Haoran Liu, Jin Zhou, Bo Li et al.
3 citations
Europhysics Letters (EPL)
Neural Networks Stability and Synchronization
1.05
article

Pinning synchronization of a complex network: Nodes, edges and higher-order edges

Jinhu Lü, Haoran Liu, Jin Zhou, Bo Li, QiShu Jiang
article en
3 citations

Abstract

Abstract In recent years, the interdisciplinary study of complex networks has become increasingly important in fields ranging from biology and physics to sociology and mathematics. This paper focuses on pinning control, an approach essential for achieving coordinated behavior in dynamic networks. We explore recent advancements in pinning control strategies, explaining theoretical frameworks and simulation techniques. Additionally, we discuss the significance of certain structures within networks across different orders. Finally, we conclude with a summary of key insights and propose our outlook on future research.

Europhysics Letters (EPL)Vol. 147(6)
Wuhan University (CN)
Key Programme
Quality Education
Openalex Percentile: Top 22%
Neural Networks Stability and Synchronization
1.05
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