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|Title:||Robustness evaluation of multipartite complex networks based on percolation theory||Authors:||Cai, Qing
|Keywords:||Engineering::Aeronautical engineering||Issue Date:||2020||Source:||Cai, Q., Alam, S., Pratama, M. & Liu, J. (2020). Robustness evaluation of multipartite complex networks based on percolation theory. IEEE Transactions On Systems, Man, and Cybernatics: Systems. https://dx.doi.org/10.1109/TSMC.2019.2960156||Journal:||IEEE Transactions on Systems, Man, and Cybernatics: Systems||Abstract:||To investigate the robustness of complex networks in face of disturbances can help prevent potential network disasters. Percolation on networks is a potent instrument for network robustness analysis. However, existing percolation theories are primarily developed for interdependent or multilayer networks. Little attention is paid to multipartite networks which are an indispensable part of complex networks. In this article, we theoretically explore the robustness of multipartite networks under node failures. We put forward the generic percolation theory for gauging the robustness of multipartite networks with arbitrary degree distributions. Our developed theory is capable of quantifying the robustness of multipartite networks under either random or target node attacks. Our theory unravels the second order phase transition phenomenon for multipartite networks. In order to verify the correctness of the proposed theory, simulations on computer generated multipartite networks have been carried out. The experiments demonstrate that the simulation results coincide quite well with that yielded by the proposed theory.||URI:||https://hdl.handle.net/10356/147383||ISSN:||2168-2216||DOI:||10.1109/TSMC.2019.2960156||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 in other works. The published version is available at: https://doi.org/10.1109/TSMC.2019.2960156||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||ATMRI Journal Articles|
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