Percolation theories for multipartite networked systems under random failures
Real-world complex systems inevitably suffer from perturbations. When some system components break down and trigger cascading failures on a system, the system will be out of control. In order to assess the tolerance of complex systems to perturbations, an effective way is to model a system as a netw...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/144368 |
| _version_ | 1826121066026631168 |
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| author | Cai, Qing Alam, Sameer Pratama, Mahardhika Wang, Zhen |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Cai, Qing Alam, Sameer Pratama, Mahardhika Wang, Zhen |
| author_sort | Cai, Qing |
| collection | NTU |
| description | Real-world complex systems inevitably suffer from perturbations. When some system components break down and trigger cascading failures on a system, the system will be out of control. In order to assess the tolerance of complex systems to perturbations, an effective way is to model a system as a network composed of nodes and edges and then carry out network robustness analysis. Percolation theories have proven as one of the most effective ways for assessing the robustness of complex systems. However, existing percolation theories are mainly for multilayer or interdependent networked systems, while little attention is paid to complex systems that are modeled as multipartite networks. This paper fills this void by establishing the percolation theories for multipartite networked systems under random failures. To achieve this goal, this paper first establishes two network models to describe how cascading failures propagate on multipartite networks subject to random node failures. Afterward, this paper adopts the largest connected component concept to quantify the networks’ robustness. Finally, this paper develops the corresponding percolation theories based on the developed network models. Simulations on computer-generated multipartite networks demonstrate that the proposed percolation theories coincide quite well with the simulations. |
| first_indexed | 2024-10-01T05:26:40Z |
| format | Journal Article |
| id | ntu-10356/144368 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:26:40Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1443682020-11-07T20:10:49Z Percolation theories for multipartite networked systems under random failures Cai, Qing Alam, Sameer Pratama, Mahardhika Wang, Zhen School of Computer Science and Engineering School of Mechanical and Aerospace Engineering Air Traffic Management Research Institute Engineering::Aeronautical engineering Air Traffic Management Cascading Failures Real-world complex systems inevitably suffer from perturbations. When some system components break down and trigger cascading failures on a system, the system will be out of control. In order to assess the tolerance of complex systems to perturbations, an effective way is to model a system as a network composed of nodes and edges and then carry out network robustness analysis. Percolation theories have proven as one of the most effective ways for assessing the robustness of complex systems. However, existing percolation theories are mainly for multilayer or interdependent networked systems, while little attention is paid to complex systems that are modeled as multipartite networks. This paper fills this void by establishing the percolation theories for multipartite networked systems under random failures. To achieve this goal, this paper first establishes two network models to describe how cascading failures propagate on multipartite networks subject to random node failures. Afterward, this paper adopts the largest connected component concept to quantify the networks’ robustness. Finally, this paper develops the corresponding percolation theories based on the developed network models. Simulations on computer-generated multipartite networks demonstrate that the proposed percolation theories coincide quite well with the simulations. Published version 2020-11-02T04:32:41Z 2020-11-02T04:32:41Z 2020 Journal Article Cai, Q., Alam, S., Pratama, M., & Wang, Z. (2020). Percolation theories for multipartite networked systems under random failures. Complexity. doi:10.1155/2020/3974503 1076-2787 https://hdl.handle.net/10356/144368 10.1155/2020/3974503 en Complexity © 2020 Qing Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. application/pdf |
| spellingShingle | Engineering::Aeronautical engineering Air Traffic Management Cascading Failures Cai, Qing Alam, Sameer Pratama, Mahardhika Wang, Zhen Percolation theories for multipartite networked systems under random failures |
| title | Percolation theories for multipartite networked systems under random failures |
| title_full | Percolation theories for multipartite networked systems under random failures |
| title_fullStr | Percolation theories for multipartite networked systems under random failures |
| title_full_unstemmed | Percolation theories for multipartite networked systems under random failures |
| title_short | Percolation theories for multipartite networked systems under random failures |
| title_sort | percolation theories for multipartite networked systems under random failures |
| topic | Engineering::Aeronautical engineering Air Traffic Management Cascading Failures |
| url | https://hdl.handle.net/10356/144368 |
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