Hypergraph-Based High-Speed Rail Hypernetwork Analysis and Node Importance Evaluation Using Operational Data: A Case Study of China

The high-speed railway (HSR) network is pivotal to transportation infrastructure, yet its resilience and node importance under complex operational dynamics remain to be explored. This study proposes a hypergraph-based framework to analyze the Chinese HSR hypernetwork and evaluate station importance using real-world operational data. First, we construct the HSR hypernetwork with 858 stations and 2,736 routes, capturing multinode interdependencies through hyperedges. Five metrics—degree, hyperdegree, betweenness centrality, closeness centrality, and a novel weighted adjacency hyperdegree—are integrated to quantify topological characteristics. To address the limitations of conventional methods, we introduce the weighted adjacency hyperdegree, which incorporates neighborhood influence and route semantics. The entropy-weighted TOPSIS method is refined with a central city impact adjustment. Empirical results reveal that the Yangtze River Delta exhibits the densest HSR connectivity, while key hubs like Zhengzhoudong Station dominate due to their bridging roles and high accessibility. The proposed framework identifies latent critical nodes overlooked by traditional metrics, such as Jiangningxi, which gains prominence through synergistic interactions with adjacent hubs. Validation via targeted attacks demonstrates that the TOPSIS-based method accelerates network fragmentation, underscoring its effectiveness in resilience planning. This work provides actionable insights for optimizing HSR infrastructure, enhancing disaster response, and balancing regional development.