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Title: Percolation theories for multipartite networked systems under random failures
Authors: Cai, Qing
Alam, Sameer
Pratama, Mahardhika
Wang, Zhen
Keywords: Engineering::Aeronautical engineering
Issue Date: 2020
Source: Cai, Q., Alam, S., Pratama, M., & Wang, Z. (2020). Percolation theories for multipartite networked systems under random failures. Complexity. doi:10.1155/2020/3974503
Journal: Complexity 
Abstract: 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.
ISSN: 1076-2787
DOI: 10.1155/2020/3974503
Schools: School of Computer Science and Engineering 
School of Mechanical and Aerospace Engineering 
Research Centres: Air Traffic Management Research Institute 
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Journal Articles

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