The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks
Many real infrastructure systems such as power grids and communication networks across cities not only depend on each other but also have community structures. This observation derives a new research subject of the interdependent community networks (ICNs). Recent works showed that the ICNs are extre...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/1646930 |
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| author | Kai Gong Yu Huang Xiao-long Chen Qing Li Ming Tang |
| author_facet | Kai Gong Yu Huang Xiao-long Chen Qing Li Ming Tang |
| author_sort | Kai Gong |
| collection | DOAJ |
| description | Many real infrastructure systems such as power grids and communication networks across cities not only depend on each other but also have community structures. This observation derives a new research subject of the interdependent community networks (ICNs). Recent works showed that the ICNs are extremely vulnerable to the failure of interconnected nodes between communities. Such vulnerability is prone to cause avalanche breakdown of the ICNs. How to improve the robustness of ICNs remains a challenge. In this paper, we propose a new target recovery strategy in the self-awareness recovery model, called recovery strategy based on community structures (RCS). The self-awareness recovery model repairs and reactivates the original pair of failed nodes that belong to mutual boundary of networks during cascading failures. The key insight is that the RCS explicitly considers both intercommunity links and intracommunity links. In this paper, we compare RCS with the state-of-the-art approaches based on randomness, degree centrality, and local centrality. We find that the RCS outperforms the other three strategies on the size of giant component, the existence probability of giant component, the number of iterative cascade steps, and the average degree of the remaining network. Moreover, RCS is robust against a given noise, and the optimal parameter of RCS remains stable even if the recovery ratio varies. |
| format | Article |
| id | doaj-art-45ba6cc0e2d4483c9ce9bae3a2e3e213 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-45ba6cc0e2d4483c9ce9bae3a2e3e2132025-02-03T06:43:38ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/16469301646930The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community NetworksKai Gong0Yu Huang1Xiao-long Chen2Qing Li3Ming Tang4School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, Sichuan, ChinaSchool of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, Sichuan, ChinaSchool of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, Sichuan, ChinaSichuan Key Laboratory of Financial Intelligence and Financial Engineering, Southwestern University of Finance and Economics, Chengdu, Sichuan, ChinaSchool of Physics and Electronic Science, East China Normal University, Shanghai, ChinaMany real infrastructure systems such as power grids and communication networks across cities not only depend on each other but also have community structures. This observation derives a new research subject of the interdependent community networks (ICNs). Recent works showed that the ICNs are extremely vulnerable to the failure of interconnected nodes between communities. Such vulnerability is prone to cause avalanche breakdown of the ICNs. How to improve the robustness of ICNs remains a challenge. In this paper, we propose a new target recovery strategy in the self-awareness recovery model, called recovery strategy based on community structures (RCS). The self-awareness recovery model repairs and reactivates the original pair of failed nodes that belong to mutual boundary of networks during cascading failures. The key insight is that the RCS explicitly considers both intercommunity links and intracommunity links. In this paper, we compare RCS with the state-of-the-art approaches based on randomness, degree centrality, and local centrality. We find that the RCS outperforms the other three strategies on the size of giant component, the existence probability of giant component, the number of iterative cascade steps, and the average degree of the remaining network. Moreover, RCS is robust against a given noise, and the optimal parameter of RCS remains stable even if the recovery ratio varies.http://dx.doi.org/10.1155/2020/1646930 |
| spellingShingle | Kai Gong Yu Huang Xiao-long Chen Qing Li Ming Tang The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks Complexity |
| title | The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks |
| title_full | The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks |
| title_fullStr | The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks |
| title_full_unstemmed | The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks |
| title_short | The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks |
| title_sort | target recovery strategy for preventing avalanche breakdown on interdependent community networks |
| url | http://dx.doi.org/10.1155/2020/1646930 |
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