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Percolation on Networks with Conditional Dependence Group

Recently, the dependence group has been proposed to study the robustness of networks with interdependent nodes. A dependence group means that a failed node in the group can lead to the failures of the whole group. Considering the situation of real networks that one failed node may not always break t...

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Detalles Bibliográficos
Autores principales: Wang, Hui, Li, Ming, Deng, Lin, Wang, Bing-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433190/
https://www.ncbi.nlm.nih.gov/pubmed/25978634
http://dx.doi.org/10.1371/journal.pone.0126674
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author Wang, Hui
Li, Ming
Deng, Lin
Wang, Bing-Hong
author_facet Wang, Hui
Li, Ming
Deng, Lin
Wang, Bing-Hong
author_sort Wang, Hui
collection PubMed
description Recently, the dependence group has been proposed to study the robustness of networks with interdependent nodes. A dependence group means that a failed node in the group can lead to the failures of the whole group. Considering the situation of real networks that one failed node may not always break the functionality of a dependence group, we study a cascading failure model that a dependence group fails only when more than a fraction β of nodes of the group fail. We find that the network becomes more robust with the increasing of the parameter β. However, the type of percolation transition is always first order unless the model reduces to the classical network percolation model, which is independent of the degree distribution of the network. Furthermore, we find that a larger dependence group size does not always make the networks more fragile. We also present exact solutions to the size of the giant component and the critical point, which are in agreement with the simulations well.
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spelling pubmed-44331902015-05-27 Percolation on Networks with Conditional Dependence Group Wang, Hui Li, Ming Deng, Lin Wang, Bing-Hong PLoS One Research Article Recently, the dependence group has been proposed to study the robustness of networks with interdependent nodes. A dependence group means that a failed node in the group can lead to the failures of the whole group. Considering the situation of real networks that one failed node may not always break the functionality of a dependence group, we study a cascading failure model that a dependence group fails only when more than a fraction β of nodes of the group fail. We find that the network becomes more robust with the increasing of the parameter β. However, the type of percolation transition is always first order unless the model reduces to the classical network percolation model, which is independent of the degree distribution of the network. Furthermore, we find that a larger dependence group size does not always make the networks more fragile. We also present exact solutions to the size of the giant component and the critical point, which are in agreement with the simulations well. Public Library of Science 2015-05-15 /pmc/articles/PMC4433190/ /pubmed/25978634 http://dx.doi.org/10.1371/journal.pone.0126674 Text en © 2015 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Wang, Hui
Li, Ming
Deng, Lin
Wang, Bing-Hong
Percolation on Networks with Conditional Dependence Group
title Percolation on Networks with Conditional Dependence Group
title_full Percolation on Networks with Conditional Dependence Group
title_fullStr Percolation on Networks with Conditional Dependence Group
title_full_unstemmed Percolation on Networks with Conditional Dependence Group
title_short Percolation on Networks with Conditional Dependence Group
title_sort percolation on networks with conditional dependence group
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433190/
https://www.ncbi.nlm.nih.gov/pubmed/25978634
http://dx.doi.org/10.1371/journal.pone.0126674
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