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Cited by 954

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Pinning a Complex Dynamical Network to Its Equilibrium

Li, X., Wang, X., Chen, G.·2004·IEEE Transactions on Circuits and Systems I: Regular Papers

Abstract

It is now known that the complexity of network topology has a great impact on the stabilization of complex dynamical networks. In this work, we study the control of random networks and scale-free networks. Conditions are investigated for globally or locally stabilizing such networks. Our strategy is to apply local feedback control to a small fraction of network nodes. We propose the concept of virtual control for microscopic dynamics throughout the process with different pinning schemes for both random networks and scale-free networks. We explain the main reason why significantly less local controllers are required by specifically pinning the most highly connected nodes in a scale-free network than those required by the randomly pinning scheme, and why there is no significant difference between specifically and randomly pinning schemes for controlling random dynamical networks. We also study the synchronization phenomenon of controlled dynamical networks in the stabilization process, both analytically and numerically.

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Metadata

Authors
Li, X., Wang, X., & Chen, G.
Venue
IEEE Transactions on Circuits and Systems I: Regular Papers
Published
2004-10
Volume
51
Issue
10
Pages
2074-2087
ISSN
1057-7122 (ISSN-L)
OpenAlex
W2113127719
Cited by
954
References
3
FWCI
17.28
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Pinning a Complex Dynamical Network to Its Equilibrium. https://app.cochat.ai/papers/10.1109/tcsi.2004.835655
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Li, X., Wang, X., & Chen, G. (2004). Pinning a Complex Dynamical Network to Its Equilibrium. IEEE Transactions on Circuits and Systems I: Regular Papers. https://doi.org/10.1109/tcsi.2004.835655

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.