Robustness Of Network Controllability Against Cascading Failure

INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING: BIG DATA AND MACHINE LEARNING, PT II(2019)

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摘要
Controllability of networks widely existing in real-life systems have been a critical and attractive research subject for both network science and control systems communities. Research in network controllability has mostly focused on the effects of the network structure on its controllability, and some studies have begun to investigate the controllability robustness of complex networks. Cascading failure is common phenomenon in many infrastructure networks, which largely affect normal operation of networks, and sometimes even lead to collapse, resulting in considerable economic losses. The robustness of network controllability against the cascading failure is studied by a linear load-capacity model with a breakdown probability in this paper. The controllability of canonical model networks under different node attack strategies is investigated, random failure and malicious attack. It is shown by numerical simulations that the tolerant parameter of load-capacity model has an important role in the emergence of cascading failure, independent to the types of network. The networks with moderate average degree are more vulnerable to the cascading failure while these with high average degree are very robust. In particular, betweenness attack strategy is more harmful to the network controllability than degree attack one, especially for the scale-free networks.
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关键词
Controllability, Robustness, Cascading failure, Complex networks
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