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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3974503 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850220136869396480 |
|---|---|
| author | Qing Cai Sameer Alam Mahardhika Pratama Zhen Wang |
| author_facet | Qing Cai Sameer Alam Mahardhika Pratama Zhen Wang |
| author_sort | Qing Cai |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-7764f696368f4613982a6dac189cc623 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7764f696368f4613982a6dac189cc6232025-08-20T02:07:09ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/39745033974503Percolation Theories for Multipartite Networked Systems under Random FailuresQing Cai0Sameer Alam1Mahardhika Pratama2Zhen Wang3School of Mechanical and Aerospace Engineering, Nanyang Technological University, SingaporeSchool of Mechanical and Aerospace Engineering, Nanyang Technological University, SingaporeSchool of Computer Science and Engineering, Nanyang Technological University, SingaporeSchool of Mechanical Engineering and Center for Optical Imagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, ChinaReal-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.http://dx.doi.org/10.1155/2020/3974503 |
| spellingShingle | Qing Cai Sameer Alam Mahardhika Pratama Zhen Wang Percolation Theories for Multipartite Networked Systems under Random Failures Complexity |
| 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 |
| url | http://dx.doi.org/10.1155/2020/3974503 |
| work_keys_str_mv | AT qingcai percolationtheoriesformultipartitenetworkedsystemsunderrandomfailures AT sameeralam percolationtheoriesformultipartitenetworkedsystemsunderrandomfailures AT mahardhikapratama percolationtheoriesformultipartitenetworkedsystemsunderrandomfailures AT zhenwang percolationtheoriesformultipartitenetworkedsystemsunderrandomfailures |