RESEARCH PAPER
An intelligent key nodes identification method in transportation networks based on gated attention multi-channel GCN
1
Department of Intelligence Manufacturing Engineering, Xi'an University of Science and Technology, China
2
Department of Industrial Engineering, Zhengzhou University, China
Submission date: 2025-11-25
Final revision date: 2026-01-09
Acceptance date: 2026-02-13
Online publication date: 2026-02-23
Corresponding author
Zhenggeng Ye
Department of Industrial Engineering, Zhengzhou University, 450001, Zhengzhou, Henan, China
Department of Industrial Engineering, Zhengzhou University, 450001, Zhengzhou, Henan, China
KEYWORDS
TOPICS
ABSTRACT
The key nodes in Transportation systems can improve the transportation system's performance efficiently and quickly when the maintenance resources are limited. A gated attention multi-channel graph convolutional network (KeyGAM-GCN) is proposed to identify the key nodes for complex transportation networks, which is an intelligent data-driven unsupervised key nodes identification method. In KeyGAM-GCN, a multi-channel graph convolutional network is developed to extract diverse topological and attribute features from transportation networks. A gated attention mechanism can fuse features by adaptively balancing the importance of different feature channels. To validate the effectiveness, experiments on 10 real-world transportation datasets are performed by comparing KeyGAM-GCN with several baselines in multiple metrics. The susceptible-infected-recovered-susceptible model is used to generate the nodes lables for evaluating the performance of the proposed method. The results show that KeyGAM-GCN can provide guidance for preventive maintenance for transportation systems.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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