Approximating the Controllability Robustness of Directed Random-graph Networks Against Random Edge-removal Attacks

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Original languageEnglish
Pages (from-to)376-388
Journal / PublicationInternational Journal of Control, Automation and Systems
Issue number2
Online published30 Jan 2023
Publication statusPublished - Feb 2023


Network controllability robustness reflects how well a networked system can maintain its controllability against destructive attacks. Its performance can be measured by a sequence of values that record the remaining controllability of the network after a sequential node-removal or edge-removal attacks. In this paper, a hybrid approximation (HyA) method is proposed to estimate the controllability robustness performance of large-scale directed random-graph (RG) networks under random edge-removal attacks. HyA sufficiently utilizes the similarity between the generation mechanism of the RG networks and the destructing process of random edge-removal attacks. Two threshold values are set to classify general RG networks as 'dense', 'sparse', or 'median', according to the average degree of each network. A two-phase approximation is applied to 'sparse' RG networks, while different three-phase approximations are applied to 'dense' and 'median' RG networks, respectively. Simulation results verify that 1) HyA is able to precisely approximate the controllability curves of RG networks under random edge-removal attacks; 2) HyA is time-efficient as compared to the conventional time-consuming attack simulations.

©ICROS, KIEE and Springer 2023

Research Area(s)

  • Complex network, controllability, directed random graph, random edge attack, robustness

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