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Clairvoyant targeted attack on complex networks

Clairvoyant targeted attack on complex networks Complex networks' resilience against attacks represents a crucial issue in terms of network structure integrity. We investigate the effect of removing nodes on the network diameter in the case of a simultaneous targeted attack and sequential targeted attack. The analysis has been implemented on several network instances, taking into account different centrality measures and clustering coefficients values. Empirical networks have also been observed to compare the effects of the two removal schemes. According to classical literature, we assume that the network attacker has a wide-ranging knowledge of the system. It can be defined as clairvoyant since it knows, a priori, of all the characteristics of the problem's instances. This awareness is not always applicable when real networks are characterised by a dynamic environment. Hence, we distinguish between clairvoyant and non-clairvoyant attacks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2018.088314
Publisher site
See Article on Publisher Site

Abstract

Complex networks' resilience against attacks represents a crucial issue in terms of network structure integrity. We investigate the effect of removing nodes on the network diameter in the case of a simultaneous targeted attack and sequential targeted attack. The analysis has been implemented on several network instances, taking into account different centrality measures and clustering coefficients values. Empirical networks have also been observed to compare the effects of the two removal schemes. According to classical literature, we assume that the network attacker has a wide-ranging knowledge of the system. It can be defined as clairvoyant since it knows, a priori, of all the characteristics of the problem's instances. This awareness is not always applicable when real networks are characterised by a dynamic environment. Hence, we distinguish between clairvoyant and non-clairvoyant attacks.

Journal

International Journal of Computational Economics and EconometricsInderscience Publishers

Published: Jan 1, 2018

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