《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (7): 85-94.doi: 10.6040/j.issn.1671-9352.1.2023.064
        
               		Xingyu HUANG1,2(
),Mingyu ZHAO1,3,Ziyu LYU1,2,*(
)
			  
			
			
			
                
        
    
摘要:
针对图神经网络(graph neural network, GNN)模型缺乏相应的探针这一问题,提出面向图神经网络表征学习的知识探测框架,基于不同领域数据的类别属性设计2种类别感知的知识探针,分别为聚类探针和对比聚类探针。2种探针分别探测不同模型的表征效果并给出相应的分数。在引用网络、社交网络和生物网络等3个邻域的8个数据集上,对7个经典的图神经网络模型的表征学习实现了系统性地知识探测和评估实验,归纳出探测和评估结论。
中图分类号:
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