Please use this identifier to cite or link to this item:
|Title:||On the robustness of complex systems with multipartitivity structures under node attacks||Authors:||Cai, Qing
|Keywords:||Engineering::Aeronautical engineering||Issue Date:||2020||Source:||Cai, Q., Alam, S., & Liu, J. (2020). On the robustness of complex systems with multipartitivity structures under node attacks. IEEE Transactions on Control of Network Systems, 7(1), 106-117. doi:10.1109/TCNS.2019.2919856||Journal:||IEEE Transactions on Control of Network Systems||Abstract:||Complex systems in the real world inevitably suffer from unpredictable perturbations, which can trigger system disasters, wreaking significant economical losses. To exploit the robustness of complex systems in the face of disturbances is of great significance. One of the most useful methods for system robustness analysis comes from the field of complex networks characterized by percolation theories. Many percolation theories, therefore, have been developed by researchers to investigate the robustness of diverse complex networks. Nevertheless, extant percolation theories are primarily devised for multilayer or interdependent networks. Little endeavor is dedicated to systems with multipartitivity structures, that is, multipartite networks, which are an indispensable part of complex networks. This paper fills this research gap by theoretically examining the robustness of multipartite networks under random or target node attacks. The generic percolation theory for robustness analysis of multipartite networks is accordingly put forward. To validate the correctness of the proposed percolation theory, we carry out simulations on computer-generated multipartite networks with Poisson degree distributions. The results yielded by the proposed theory coincide well with the simulations. Both theoretical and simulation results suggest that complex systems with multipartitivity structures could be more robust than those with multilayer structures.||URI:||https://hdl.handle.net/10356/144365||ISSN:||2325-5870||DOI:||10.1109/TCNS.2019.2919856||Rights:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work is available at: https://doi.org/10.1109/TCNS.2019.2919856||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||ATMRI Journal Articles|
Updated on Mar 9, 2021
Updated on May 5, 2021
Updated on May 5, 2021
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.