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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| Format: | Online |
| Language: | English |
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Public Library of Science
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433190/ |
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pubmed-4433190 |
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pubmed-44331902015-05-27 Percolation on Networks with Conditional Dependence Group Wang, Hui Li, Ming Deng, Lin Wang, Bing-Hong 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. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Wang, Hui Li, Ming Deng, Lin Wang, Bing-Hong |
| spellingShingle |
Wang, Hui Li, Ming Deng, Lin Wang, Bing-Hong Percolation on Networks with Conditional Dependence Group |
| author_facet |
Wang, Hui Li, Ming Deng, Lin Wang, Bing-Hong |
| author_sort |
Wang, Hui |
| title |
Percolation on Networks with Conditional Dependence Group |
| title_short |
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_sort |
percolation on networks with conditional dependence group |
| 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. |
| publisher |
Public Library of Science |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433190/ |
| _version_ |
1613224049521983488 |