Published November 2015 | Version v1
Journal article

Effect of clustering on attack vulnerability of interdependent scale-free networks

Description

In order to deeply understand the complex interdependent systems, it is of great concern to take clustering coefficient, which is an important feature of many real-world systems, into account. Previous study mainly focused on the impact of clustering on interdependent networks under random attacks, while we extend the study to the case of the more realistic attacking strategy, targeted attack. A system composed of two interdependent scale-free networks with tunable clustering is provided. The effects of coupling strength and coupling preference on attack vulnerability are explored. Numerical simulation results demonstrate that interdependent links between two networks make the entire system much more fragile to attacks. Also, it is found that clustering significantly increases the vulnerability of interdependent scale-free networks. Moreover, for fully coupled network, disassortative coupling is found to be most vulnerable to random attacks, while the random and assortative coupling have little difference. Additionally, enhancing coupling strength can greatly enhance the fragility of interdependent networks against targeted attacks. These results can not only improve the deep understanding of structural complexity of complex systems, but also provide insights into the guidance of designing resilient infrastructures.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.chaos.2015.06.022

Additional details

Identifiers

DOI
10.1016/j.chaos.2015.06.022;
PII
S0960-0779(15)00192-7;

Publishing Information

Journal Title
Chaos, Solitons and Fractals
Journal Volume
80
Journal Page Range
p. 109-116
ISSN
0960-0779

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
48001769
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
COMPUTERIZED SIMULATION; COUPLING; NEURAL NETWORKS; RANDOMNESS; VULNERABILITY
Descriptors DEC
SIMULATION

Optional Information

Copyright
Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.