Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks
This 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, su...
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MDPI AG
2025-04-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/4/101 |
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| author | Zhenguo Xu Jun Li Irene Moulitsas Fangqu Niu |
| author_facet | Zhenguo Xu Jun Li Irene Moulitsas Fangqu Niu |
| author_sort | Zhenguo Xu |
| collection | DOAJ |
| description | This 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. |
| format | Article |
| id | doaj-art-e8e61df9dc424a059558d2f5b7f862d0 |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-e8e61df9dc424a059558d2f5b7f862d02025-08-20T02:28:19ZengMDPI AGBig Data and Cognitive Computing2504-22892025-04-019410110.3390/bdcc9040101Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional NetworksZhenguo Xu0Jun Li1Irene Moulitsas2Fangqu Niu3Centre for Computational Engineering Sciences, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UKCentre for Computational Engineering Sciences, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UKCentre for Computational Engineering Sciences, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UKInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaThis 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.https://www.mdpi.com/2504-2289/9/4/101key node identificationcommunity detectionlink predictionsmall-world networkgraph attention networksvariational graph autoencoder |
| spellingShingle | Zhenguo Xu Jun Li Irene Moulitsas Fangqu Niu Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks Big Data and Cognitive Computing key node identification community detection link prediction small-world network graph attention networks variational graph autoencoder |
| title | Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks |
| title_full | Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks |
| title_fullStr | Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks |
| title_full_unstemmed | Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks |
| title_short | Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks |
| title_sort | analysis of china s high speed railway network using complex network theory and graph convolutional networks |
| topic | key node identification community detection link prediction small-world network graph attention networks variational graph autoencoder |
| url | https://www.mdpi.com/2504-2289/9/4/101 |
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