Control Capacity and A Random Sampling Method in Exploring Controllability of Complex Networks

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Control Capacity and A Random Sampling Method in Exploring Controllability of Complex Networks

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Title: Control Capacity and A Random Sampling Method in Exploring Controllability of Complex Networks
Author: Jia, Tao; Barabási, Albert-László

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Citation: Jia, Tao, and Albert-László Barabási. 2013. “Control Capacity and A Random Sampling Method in Exploring Controllability of Complex Networks.” Scientific Reports 3 (1): 2354. doi:10.1038/srep02354. http://dx.doi.org/10.1038/srep02354.
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Abstract: Controlling complex systems is a fundamental challenge of network science. Recent advances indicate that control over the system can be achieved through a minimum driver node set (MDS). The existence of multiple MDS's suggests that nodes do not participate in control equally, prompting us to quantify their participations. Here we introduce control capacity quantifying the likelihood that a node is a driver node. To efficiently measure this quantity, we develop a random sampling algorithm. This algorithm not only provides a statistical estimate of the control capacity, but also bridges the gap between multiple microscopic control configurations and macroscopic properties of the network under control. We demonstrate that the possibility of being a driver node decreases with a node's in-degree and is independent of its out-degree. Given the inherent multiplicity of MDS's, our findings offer tools to explore control in various complex systems.
Published Version: doi:10.1038/srep02354
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3733055/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11855796
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