Analysis of China’s high-speed railway network using complex network theory and graph convolutional networks

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dc.contributor.authorXu, Zhenguo
dc.contributor.authorLi, Jun
dc.contributor.authorMoulitsas, Irene
dc.contributor.authorNiu, Fangqu
dc.date.accessioned2025-05-02T09:59:07Z
dc.date.available2025-05-02T09:59:07Z
dc.date.freetoread2025-05-02
dc.date.issued2025-04-16
dc.date.pubOnline2025-04-16
dc.description.abstractThis 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.
dc.description.journalNameBig Data and Cognitive Computing
dc.identifier.citationXu 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 101
dc.identifier.eissn2504-2289
dc.identifier.elementsID672853
dc.identifier.issn2504-2289
dc.identifier.issueNo4
dc.identifier.paperNo101
dc.identifier.urihttps://doi.org/10.3390/bdcc9040101
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23850
dc.identifier.volumeNo9
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2504-2289/9/4/101
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject7 Affordable and Clean Energy
dc.subject46 Information and computing sciences
dc.subjectkey node identification
dc.subjectcommunity detection
dc.subjectlink prediction
dc.subjectsmall-world network
dc.subjectgraph attention networks
dc.subjectvariational graph autoencoder
dc.subjectnetwork efficiency and robustness
dc.titleAnalysis of China’s high-speed railway network using complex network theory and graph convolutional networks
dc.typeArticle
dcterms.dateAccepted2025-04-11

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