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

Yang Lou; Lin Wang; Shengli Xie; Guanrong Chen

doi:10.1007/s12555-021-0831-4

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

Yang Lou
, Lin Wang
, Shengli Xie
, Guanrong Chen^*

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

7 Citations (Scopus)

Abstract

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

Original language	English
Pages (from-to)	376-388
Journal	International Journal of Control, Automation and Systems
Volume	21
Issue number	2
Online published	30 Jan 2023
DOIs	https://doi.org/10.1007/s12555-021-0831-4
Publication status	Published - Feb 2023

Funding

This research was supported in part by the National Natural Science Foundation of China (No. 62002249, 61873167), in part by the Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P. R. China (No. Scip202103), in part by the Lam Woo Research Fund at Lingnan University (No. LWP20012), and in part by the Hong Kong Research Grants Council under the GRF Grant CityU11206320.

Research Keywords

Complex network
controllability
directed random graph
random edge attack
robustness

RGC Funding Information

RGC-funded

Access Output

10.1007/s12555-021-0831-4

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{309a3a7ee61b4b4d90116f1ba908b388,

title = "Approximating the Controllability Robustness of Directed Random-graph Networks Against Random Edge-removal Attacks",

abstract = "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. {\textcopyright}ICROS, KIEE and Springer 2023",

keywords = "Complex network, controllability, directed random graph, random edge attack, robustness",

author = "Yang Lou and Lin Wang and Shengli Xie and Guanrong Chen",

year = "2023",

month = feb,

doi = "10.1007/s12555-021-0831-4",

language = "English",

volume = "21",

pages = "376--388",

journal = "International Journal of Control, Automation and Systems",

issn = "1598-6446",

publisher = "Institute of Control, Robotics and Systems",

number = "2",

}

TY - JOUR

T1 - Approximating the Controllability Robustness of Directed Random-graph Networks Against Random Edge-removal Attacks

AU - Lou, Yang

AU - Wang, Lin

AU - Xie, Shengli

AU - Chen, Guanrong

PY - 2023/2

Y1 - 2023/2

N2 - 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

AB - 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

KW - Complex network

KW - controllability

KW - directed random graph

KW - random edge attack

KW - robustness

UR - http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000924927600005

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0- 85147025893&origin=recordpage

UR - https://www.scopus.com/pages/publications/85147025893

U2 - 10.1007/s12555-021-0831-4

DO - 10.1007/s12555-021-0831-4

M3 - RGC 21 - Publication in refereed journal

SN - 1598-6446

VL - 21

SP - 376

EP - 388

JO - International Journal of Control, Automation and Systems

JF - International Journal of Control, Automation and Systems

IS - 2

ER -

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

Abstract

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

GRF: Analyzing the Robustness of Network Controllability against Malicious Attacks

Cite this

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

Abstract

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: Analyzing the Robustness of Network Controllability against Malicious Attacks

Cite this