Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (3): 137-143.doi: 10.6040/j.issn.1672-3961.0.2025.034

• Machine Learning & Data Mining • Previous Articles     Next Articles

Graph node classification algorithm based on multi-level core aggregation GNN

TANG Kai1, WANG Fang1*, LIU Jianxia2   

  1. TANG Kai1, WANG Fang1*, LIU Jianxia2(1. School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China;
    2. School of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • Published:2026-06-09

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Abstract: To address the problems of insufficient utilization of neighbor structure information and feature blurring caused by multi-layer propagation in node classification tasks, a multi-level core aggregation graph neural network(MCAG)model was proposed. The MCAG model divided the graph data into two-node and multi-node connected components. A custom core node selection mechanism was used to construct a total core node set, from which a core node subgraph was then extended. These subgraphs were combined to form a core node aggregation layer, which was fused with a GraphSAGE layer and a group normalization layer to obtain the final node representations. Experimental results showed that the MCAG model improved the node classification accuracy by an average of 3.28% on six datasets, including Cora. On the Amap dataset, the model performed comparably to baseline models, demonstrated stable overall performance.The training time was reduced by an average of 50% compared to the original architecture, and the performance of the core node set sampling method was superior to that of random walk sampling. These findings verified the effectiveness and superiority of the MCAG model.

Key words: multi-level core aggregation, selection mechanism, core node, node classification, graph neural network

CLC Number: 

  • TP391
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