Identification of key nodes and vulnerability analysis in airport networks with attention mechanism - Guangjian Ren; Zongqian Zhang; Yanhua Li (Scientific Reports)
Abstract As the core hub of air transport, the efficient operation of airports depends on the accurate identification and timely management of key nodes. In this paper, an airport centrality measure based on the attention mechanism is proposed. In this method, the core indicators such as degree, betweenness, closeness, eigenvector, and throughput centrality are adopted, and the input layer is constructed by normalization processing. The query, key and value vectors are calculated by a linear transformation, and then the attention score and weight coefficient are obtained, and finally, the comprehensive importance of the airport is evaluated. Taking the airport group in Eastern China as an example, the differences in centrality indicators are demonstrated, and it is proved that the proposed centrality can integrate the differences of single indicators and have a stronger correlation with key indicators. In the vulnerability analysis of the airport network, the results of sequencing attacks based on the proposed centrality further verify its effectiveness and reliability. This study not only provides a new perspective and tool for identifying key nodes in airports but also promotes the deepening of airport network vulnerability analysis. It has important theoretical and practical significance for improving the stability of airline networks and enhancing airport operation and management capabilities.