ENERGY COST FOR TARGET CONTROL OF COMPLEX NETWORKS

Gaopeng Duan, Aming Li, Tao Meng and Long Wang
Additional contact information
Gaopeng Duan: Center for Systems and Control, College of Engineering, Peking University, 5 Yiheyuan Road, Haidian District, Beijing, P. R. China
Aming Li: #x2020;Department of Zoology, University of Oxford, 11a Mansfield Road, Oxford OX1 3PS, UK‡Department of Biochemistry, University of Oxford, 3 South Parks Road, Oxford OX1 3QU, UK
Tao Meng: Center for Systems and Control, College of Engineering, Peking University, 5 Yiheyuan Road, Haidian District, Beijing, P. R. China
Long Wang: Center for Systems and Control, College of Engineering, Peking University, 5 Yiheyuan Road, Haidian District, Beijing, P. R. China

Advances in Complex Systems (ACS), 2020, vol. 22, issue 07n08, 1-27

Abstract: To promote the implementation of realistic control over various complex networks, recent work has been focusing on analyzing energy cost. Indeed, the energy cost quantifies how much effort is required to drive the system from one state to another when it is fully controllable. A fully controllable system means that the system can be driven by external inputs from any initial state to any final state in finite time. However, it is prohibitively expensive and unnecessary to confine that the system is fully controllable when we merely need to accomplish the so-called target control — controlling a subnet of nodes chosen from the entire network. Yet, when the system is partially controllable, the associated energy cost remains elusive. Here we present the minimum energy cost for controlling an arbitrary subset of nodes of a network. We show the scaling behavior of the precise upper and lower bounds of the minimum energy in terms of the time given to accomplish control. For controlling a given number of target nodes, we further show that the associated energy over different configurations can differ by several orders of magnitude. When the adjacency matrix of the network is nonsingular, we can simplify the framework by just considering the induced subgraph spanned by target nodes instead of the entire network. Importantly, we find that energy cost could be saved by orders of magnitude as we only need the partial controllability of the entire network. Our theoretical results are all corroborated by numerical calculations, and pave the way for estimating the energy cost to implement realistic target control in various applications.

Keywords: Complex networks; controllability; target control; energy cost (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S021952591950022X

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