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• W4323312906 startingPage "113310" @default.
• W4323312906 abstract "As a kind of complex networks, airline network has been studied by more and more scholars. How to identify influential airports in airline network is an open issue, which has become a far-reaching direction in its research and development. Many centrality measures have been improved and proposed to address it. However, the majority of studies only focused on one centrality measure, and each centrality measure has its own drawbacks and restrictions. In order to address this issue, learning to the evaluation and analysis principle of failure mode and effects analysis (FMEA), we think that the influence of airports can be measured by their possible effects after failure in the airline network. In this paper, a novel method for identifying influential airports is proposed. Firstly, three risk factors of airport’s failure mode are defined based on complex network theory and the transportation mechanism of airports in airline network flight flow. Then, the effects analyses of three failure risk factors of each airport are made. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) algorithm is utilized to fuse the effects of the three defined failure risk factors to determine the comprehensive influence of airport in airline network. To evaluate the performance of the proposed method, the Susceptible–Infected (SI) model is applied to examine the spreading influence of the airports ranked by various centrality measures. The comparison experimental conducted on four airline networks illustrate the effectiveness of the proposed method." @default.
• W4323312906 created "2023-03-07" @default.
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• W4323312906 date "2023-04-01" @default.
• W4323312906 modified "2023-10-06" @default.
• W4323312906 title "Identifying influential airports in airline network based on failure risk factors with TOPSIS" @default.
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• W4323312906 doi "https://doi.org/10.1016/j.chaos.2023.113310" @default.
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