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Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks
The study of interdependent networks has become a new research focus in recent years. We focus on one fundamental property of interdependent networks: vulnerability. Previous studies mainly focused on the impact of topological properties upon interdependent networks under random attacks, the effect...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016735/ https://www.ncbi.nlm.nih.gov/pubmed/27609483 http://dx.doi.org/10.1038/srep32983 |
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author | Sun, Shiwen Wu, Yafang Ma, Yilin Wang, Li Gao, Zhongke Xia, Chengyi |
author_facet | Sun, Shiwen Wu, Yafang Ma, Yilin Wang, Li Gao, Zhongke Xia, Chengyi |
author_sort | Sun, Shiwen |
collection | PubMed |
description | The study of interdependent networks has become a new research focus in recent years. We focus on one fundamental property of interdependent networks: vulnerability. Previous studies mainly focused on the impact of topological properties upon interdependent networks under random attacks, the effect of degree heterogeneity on structural vulnerability of interdependent networks under intentional attacks, however, is still unexplored. In order to deeply understand the role of degree distribution and in particular degree heterogeneity, we construct an interdependent system model which consists of two networks whose extent of degree heterogeneity can be controlled simultaneously by a tuning parameter. Meanwhile, a new quantity, which can better measure the performance of interdependent networks after attack, is proposed. Numerical simulation results demonstrate that degree heterogeneity can significantly increase the vulnerability of both single and interdependent networks. Moreover, it is found that interdependent links between two networks make the entire system much more fragile to attacks. Enhancing coupling strength between networks can greatly increase the fragility of both networks against targeted attacks, which is most evident under the case of max-max assortative coupling. Current results can help to deepen the understanding of structural complexity of complex real-world systems. |
format | Online Article Text |
id | pubmed-5016735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-50167352016-09-12 Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks Sun, Shiwen Wu, Yafang Ma, Yilin Wang, Li Gao, Zhongke Xia, Chengyi Sci Rep Article The study of interdependent networks has become a new research focus in recent years. We focus on one fundamental property of interdependent networks: vulnerability. Previous studies mainly focused on the impact of topological properties upon interdependent networks under random attacks, the effect of degree heterogeneity on structural vulnerability of interdependent networks under intentional attacks, however, is still unexplored. In order to deeply understand the role of degree distribution and in particular degree heterogeneity, we construct an interdependent system model which consists of two networks whose extent of degree heterogeneity can be controlled simultaneously by a tuning parameter. Meanwhile, a new quantity, which can better measure the performance of interdependent networks after attack, is proposed. Numerical simulation results demonstrate that degree heterogeneity can significantly increase the vulnerability of both single and interdependent networks. Moreover, it is found that interdependent links between two networks make the entire system much more fragile to attacks. Enhancing coupling strength between networks can greatly increase the fragility of both networks against targeted attacks, which is most evident under the case of max-max assortative coupling. Current results can help to deepen the understanding of structural complexity of complex real-world systems. Nature Publishing Group 2016-09-09 /pmc/articles/PMC5016735/ /pubmed/27609483 http://dx.doi.org/10.1038/srep32983 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Sun, Shiwen Wu, Yafang Ma, Yilin Wang, Li Gao, Zhongke Xia, Chengyi Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks |
title | Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks |
title_full | Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks |
title_fullStr | Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks |
title_full_unstemmed | Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks |
title_short | Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks |
title_sort | impact of degree heterogeneity on attack vulnerability of interdependent networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016735/ https://www.ncbi.nlm.nih.gov/pubmed/27609483 http://dx.doi.org/10.1038/srep32983 |
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