In order to solve the problem that the graph neural network model lacks corresponding probes,a knowledge detection framework for graph neural network representation learning is proposed,and two kinds of class-aware knowledge probes are designed based on the category attributes of data in different domains,namely clustering probes and contrastive clustering probes.The two probe the characterization effect of different models and give corresponding scores.On 8 datasets in 3 neighborhoods,inclu-ding reference networks,social networks and biological networks,the representation learning of 7 classical graph neural network models realizes systematic knowledge detection and evaluation experiments,and summarizes the detection and evaluation conclu-sions.
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