Control Profiles of Complex Networks

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Science  21 Mar 2014:
Vol. 343, Issue 6177, pp. 1373-1376
DOI: 10.1126/science.1242063

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Real Network Control

Understanding how complex networks are controlled has implications for a variety of real-world networks, from traffic safety to transcriptional control. Ruths and Ruths (p. 1373; see the Perspective by Onnela) have developed a theoretical framework for analyzing individual controls within networks based on numbers of sources and sinks for information flow. By this method, the number of controls required by a network can be predicted and direct comparisons for the basis for control across networks of differing size, structure, and function can be made. Although three broad classes of real networks were observed, current, established random models of networks were insufficient to model their control structures.


Studying the control properties of complex networks provides insight into how designers and engineers can influence these systems to achieve a desired behavior. Topology of a network has been shown to strongly correlate with certain control properties; here we uncover the fundamental structures that explain the basis of this correlation. We develop the control profile, a statistic that quantifies the different proportions of control-inducing structures present in a network. We find that standard random network models do not reproduce the kinds of control profiles that are observed in real-world networks. The profiles of real networks form three well-defined clusters that provide insight into the high-level organization and function of complex systems.

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