基于解耦标签传播和多节点混合正则的图神经网络分类器

doi:10.16451/j.cnki.issn1003-6059.202505007

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Abstract Graph neural network-distilled multilayer perceptrons(MLPs) balance inference performance and efficiency in graph-related tasks to some extent. However, MLPs treat graph nodes independently and struggle to explicitly capture neighborhood information of target nodes. Thus, their inference performance is limited. To solve this problem,a graph neural network classifier based on decoupled label propagation and multi-node mixup regularization(DLPMMR) is proposed. DLPMMR trains the MLP classifier under a knowledge distillation framework to ensure basic inference performance with high inference efficiency. During the training phase, a naive and hyperparameter-free double combination strategy is employed for multi-node mixup to enhance node diversity. A mixup regularization term is then constructed to explicitly control the complexity of the MLP so as to improve its generalization ability and robustness. During the inference phase, label propagation is introduced to incorporate missing neighborhood information into the predictions of the MLP. By decoupling target nodes from their neighboring nodes, the influence of neighbor node information on the classification decision of the target node is effectively regulated, and thus the inference accuracy of MLP is further enhanced. Experiments on five benchmark graph node classification datasets demonstrate that DLPMMR exhibits strong robustness and superior performance.

Key words： Graph Neural Network Multilayer Perceptron Classifier Knowledge Distillation Label Propagation Data Augmentation

Received: 07 April 2025

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.41971340), Natural Science Foundation of Fujian Province(No.2024J01158)

Corresponding Authors: HE Wenwu, Ph.D., professor. His research interests include trust-worthy artificial intelligence and graph neural networks.

About author:: LIU Xiaoyu, Master student. Her research interests include artificial intelligence and graph neural networks.
MAO Guojun, Ph.D., professor. His research interests include artificial intelligence, data mining and distributed computing.

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Cite this article:

HE Wenwu,LIU Xiaoyu,MAO Guojun. Graph Neural Network Classifier Based onDecoupled Label Propagation and Multi-node Mixup Regularization[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(5): 472-483.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202505007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I5/472

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