Xu, ZhenguoLi, JunMoulitsas, IreneNiu, Fangqu2025-05-022025-05-022025-04-16Xu Z, Li J, Moulitsas I, Niu F. (2025) Analysis of China’s high-speed railway network using complex network theory and graph convolutional networks. Big Data and Cognitive Computing, Volume 9, Issue 4, April 2025, Article number 1012504-2289https://doi.org/10.3390/bdcc9040101https://dspace.lib.cranfield.ac.uk/handle/1826/23850This study investigated the characteristics and functionalities of China’s High-Speed Railway (HSR) network based on Complex Network Theory (CNT) and Graph Convolutional Networks (GCN). First, complex network analysis was applied to provide insights into the network’s fundamental characteristics, such as small-world properties, efficiency, and robustness. Then, this research developed three novel GCN models to identify key nodes, detect community structures, and predict new links. Findings from the complex network analysis revealed that China’s HSR network exhibits a typical small-world property, with a degree distribution that follows a log-normal pattern rather than a power law. The global efficiency indicator suggested that stations are typically connected through direct routes, while the local efficiency indicator showed that the network performs effectively within local areas. The robustness study indicated that the network can quickly lose connectivity if key nodes fail, though it showed an ability initially to self-regulate and has partially restored its structure after disruption. The GCN model for key node identification revealed that the key nodes in the network were predominantly located in economically significant and densely populated cities, positively contributing to the network’s overall efficiency and robustness. The community structures identified by the integrated GCN model highlight the economic and social connections between official urban clusters and the communities. Results from the link prediction model suggest the necessity of improving the long-distance connectivity across regions. Future work will explore the network’s socio-economic dynamics and refine and generalise the GCN models.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/46 Information and Computing Sciences7 Affordable and Clean Energy46 Information and computing scienceskey node identificationcommunity detectionlink predictionsmall-world networkgraph attention networksvariational graph autoencodernetwork efficiency and robustnessArticle2504-228967285310194