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| A Link Prediction Algorithm for Urban Public Transportation Network Based on Graph Convolution |
| YE Yanjun, YANG Shengwen, QIAN Chenhao
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| a. School of Machinery and Transportation; b. Key Laboratory of Environmental Protection and Safety of Motor Vehicles in Highland Mountainous Areas of Yunnan University, Southwest Forestry University, Kunming 650224, China |
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Abstract In order to improve the management efficiency of urban public transportation system, a complex network link prediction algorithm GCNs-LP is proposed. The algorithm integrates multi-source data, designs the meta-path random walk and skip-gram model training, obtains the low-dimensional vector representation of nodes through the feature learning of neural network, and makes link prediction based on the similarity of node vector pairs. The experimental results show that the proposed algorithm achieves better prediction performance than the baseline method (CN, PA, Node2Vec, LPNMF, etc.) on the subway network data sets of Shenzhen, Shanghai and Nanjing, and the AUC value increases by at least 29%, 14.6% and 7.1%, respectively. GCNs-LP algorithm can capture the complex structure characteristics of traffic network effectively.
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Received: 14 September 2024
Published: 14 July 2026
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