Selecting pinning nodes to control complex networked systems

Cheng, Z, Xin, Y, Cao, J, Yu, X and Lu, G 2018, 'Selecting pinning nodes to control complex networked systems', Science China Technological Sciences, vol. 61, no. 10, pp. 1537-1545.


Document type: Journal Article
Collection: Journal Articles

Title Selecting pinning nodes to control complex networked systems
Author(s) Cheng, Z
Xin, Y
Cao, J
Yu, X
Lu, G
Year 2018
Journal name Science China Technological Sciences
Volume number 61
Issue number 10
Start page 1537
End page 1545
Total pages 9
Publisher Zhongguo Kexue Zazhishe
Abstract One of the fundamental problems in pinning control of complex networks is selecting appropriate pinning nodes, such that the whole system is controlled. This is particularly useful for complex networks with huge numbers of nodes. Recent research has yielded several pinning node selection strategies, which may be efficient. However, selecting a set of pinning nodes and identifying the nodes that should be selected first remain challenging problems. In this paper, we present a network control strategy based on left Perron vector. For directed networks where nodes have the same in- and out-degrees, there has so far been no effective pinning node selection strategy, but our method can find suitable nodes. Likewise, our method also performs well for undirected networks where the nodes have the same degree. In addition, we can derive the minimum set of pinning nodes and the order in which they should be selected for given coupling strengths. Our proofs of these results depend on the properties of non-negative matrices and M-matrices. Several examples show that this strategy can effectively select appropriate pinning nodes, and that it can achieve better results for both directed and undirected networks.
Subject Control Systems, Robotics and Automation
Keyword(s) complex network
left Perron vector
M-matrices
Perron root
pinning control
DOI - identifier 10.1007/s11431-018-9319-4
Copyright notice ⃝© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018
ISSN 1674-7321
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