Energy scaling of targeted optimal control of complex networks

Isaac Klickstein (), Afroza Shirin and Francesco Sorrentino ()
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Isaac Klickstein: The University of New Mexico
Afroza Shirin: The University of New Mexico
Francesco Sorrentino: The University of New Mexico

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: Abstract Recently it has been shown that the control energy required to control a dynamical complex network is prohibitively large when there are only a few control inputs. Most methods to reduce the control energy have focused on where, in the network, to place additional control inputs. Here, in contrast, we show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, while holding the number of control signals constant, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs as long as the target set is appropriately sized. We validate our conclusions in model and real networks to arrive at an energy scaling law to better design control objectives regardless of system size, energy restrictions, state restrictions, input node choices and target node choices.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15145

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DOI: 10.1038/ncomms15145

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