Optimization of controllability and robustness of complex networks by edge directionality

Man Liang, Suoqin Jin, Dingjie Wang and Xiufen Zou ()
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Man Liang: School of Mathematics and Statistics, Wuhan University
Suoqin Jin: School of Mathematics and Statistics, Wuhan University
Dingjie Wang: School of Mathematics and Statistics, Wuhan University
Xiufen Zou: School of Mathematics and Statistics, Wuhan University

The European Physical Journal B: Condensed Matter and Complex Systems, 2016, vol. 89, issue 9, 1-8

Abstract: Abstract Recently, controllability of complex networks has attracted enormous attention in various fields of science and engineering. How to optimize structural controllability has also become a significant issue. Previous studies have shown that an appropriate directional assignment can improve structural controllability; however, the evolution of the structural controllability of complex networks under attacks and cascading has always been ignored. To address this problem, this study proposes a new edge orientation method (NEOM) based on residual degree that changes the link direction while conserving topology and directionality. By comparing the results with those of previous methods in two random graph models and several realistic networks, our proposed approach is demonstrated to be an effective and competitive method for improving the structural controllability of complex networks. Moreover, numerical simulations show that our method is near-optimal in optimizing structural controllability. Strikingly, compared to the original network, our method maintains the structural controllability of the network under attacks and cascading, indicating that the NEOM can also enhance the robustness of controllability of networks. These results alter the view of the nature of controllability in complex networks, change the understanding of structural controllability and affect the design of network models to control such networks.

Keywords: Statistical; and; Nonlinear; Physics (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1140/epjb/e2016-60845-8

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