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Title: Interdependency and vulnerability of multipartite networks under target node attacks
Authors: Cai, Qing
Pratama, Mahardhika
Alam, Sameer
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Cai, Q., Pratama, M., & Alam, S. (2019). Interdependency and vulnerability of multipartite networks under target node attacks. Complexity, 2019, 2680972-. doi:10.1155/2019/2680972
Journal: Complexity 
Abstract: Complex networks in reality may suffer from target attacks which can trigger the breakdown of the entire network. It is therefore pivotal to evaluate the extent to which a network could withstand perturbations. The research on network robustness has proven as a potent instrument towards that purpose. The last two decades have witnessed the enthusiasm on the studies of network robustness. However, existing studies on network robustness mainly focus on multilayer networks while little attention is paid to multipartite networks which are an indispensable part of complex networks. In this study, we investigate the robustness of multipartite networks under intentional node attacks. We develop two network models based on the largest connected component theory to depict the cascading failures on multipartite networks under target attacks. We then investigate the robustness of computer-generated multipartite networks with respect to eight node centrality metrics. We discover that the robustness of multipartite networks could display either discontinuous or continuous phase transitions. Interestingly, we discover that larger number of partite sets of a multipartite network could increase its robustness which is opposite to the phenomenon observed on multilayer networks. Our findings shed new lights on the robust structure design of complex systems. We finally present useful discussions on the applications of existing percolation theories that are well studied for network robustness analysis to multipartite networks. We show that existing percolation theories are not amenable to multipartite networks. Percolation on multipartite networks still deserves in-depth efforts.
ISSN: 1076-2787
DOI: 10.1155/2019/2680972
Rights: © 2019 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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