TITLE:
Identifying Key Nodes in Urban Public Transportation Networks Using Multi-Feature Fusion and GraphSAGE Network: A Case Study in Lanzhou City-China
AUTHORS:
Mingxia Qu, Huifang Feng
KEYWORDS:
Urban Public Transportation Networks, Key Node Identification, GraphSAGE, Multi-Dimensional Features, Gravity Model
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.9,
September
23,
2026
ABSTRACT: Identifying key nodes in urban public transportation networks is crucial for optimizing transportation flow and enhancing the resilience of transportation networks. Existing methods often suffer from shortcomings such as insufficient integration of global and local structural information and limited accuracy in ranking node importance. To address these issues, a multi-feature fusion model is proposed for identifying key nodes in urban public transportation networks based on the Graph Sampling and Aggregation Network (GraphSAGE). First, multi-dimensional node features are constructed from three dimensions including the network topology, operations, and spatial distribution. Second, local neighborhood features are captured by the GraphSAGE network, and a global context extraction module is constructed, through which global context information for nodes is extracted via weighted pooling layers and iterative expansion. Furthermore, a multi-layer neural network is employed to predict node importance, a gravity model is used to fuse multiple centrality metrics to generate reference labels. Finally, a ranking loss function is adopted to train the network model. An empirical analysis is conducted on the Lanzhou transportation network. The results show that the proposed method achieves a Kendall’s
τ
of 0.8568, representing a 0.86% improvement over the best baseline method, thereby validating its superiority in ranking accuracy. Vulnerability analysis further confirms the effectiveness of the proposed method in identifying key nodes. The results can provide an effective basis for decision-makers to formulate risk-resilient contingency plans for urban public transportation networks.