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Erschienen in: Cluster Computing 2/2019

24.03.2018

Importance-based entropy measures of complex networks’ robustness to attacks

verfasst von: Yu Jiang, Aiqun Hu, Jie Huang

Erschienen in: Cluster Computing | Sonderheft 2/2019

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Abstract

Intentional attacks usually cause greater damage than random failures to complex networks, so it is important to study the networks’ resilience to attacks. Although various entropy measures have been available to measure the heterogeneity of complex networks to analyze their properties, they can not distinguish the difference of robustness between the scale-free networks and random networks. Hence, we propose a new entropy measurement base on the node importance to measure complex networks’ robustness to attacks. The experimental analysis shows that the importance-based entropy measure describes the robustness properties of complex networks precisely which are in consistent with the well-known conclusion and it distinguishes the difference between the scale-free networks and random networks more obviously in the view of robustness than the existing entropy measures in the view of heterogeneity. We conclude that the entropy measurement based on the node importance is an effective measure of network’s resilience to intentional attacks and heterogeneity is not in direct relationship with the network’s resilience to errors and attacks for any network.

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Fußnoten
1
If we do not mention, the NNIE represents the NNIE based on PDB in the following text of this part.
 
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Metadaten
Titel
Importance-based entropy measures of complex networks’ robustness to attacks
verfasst von
Yu Jiang
Aiqun Hu
Jie Huang
Publikationsdatum
24.03.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 2/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2580-6

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