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2017 | OriginalPaper | Buchkapitel

5. Network Robustness Analytics with Optimization

verfasst von : Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei

Erschienen in: Computational Intelligence for Network Structure Analytics

Verlag: Springer Singapore

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Abstract

The community structure and the robustness are two important properties of networks for analyzing the functionality of complex systems. The community structure is crucial to understand the potential functionality of complex systems, while the robustness is indispensable to protect the functionality of complex systems from malicious attacks. When a network suffers from an unpredictable attack, its structural integrity would be damaged. It is essential to enhance community integrity of networks against multilevel targeted attacks. Coupled networks are extremely fragile because a node failure of a network would trigger a cascade of failures on the entire system. In reality, it is necessary to recover the damaged networks, and there are cascading failures in recovery processes. This chapter first introduces a greedy algorithm to enhance community integrity of networks against multilevel targeted attacks and then introduces a technique aiming at protecting several influential nodes for enhancing robustness of coupled networks under the recoveries.

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Titel: Network Robustness Analytics with Optimization
verfasst von: Maoguo Gong
Qing Cai
Lijia Ma
Shanfeng Wang
Yu Lei
Verlag: Springer Singapore
Buch: Computational Intelligence for Network Structure Analytics
Print ISBN: 978-981-10-4557-8

Electronic ISBN: 978-981-10-4558-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-4558-5_5

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