Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collection of graph representation learning benchmarks, it is currently not well understood what aspects of a given model are probed by them. For example, to what extent do they test the ability of a model to leverage graph structure vs. node features? Here, we develop a principled approach to taxonomize benchmarking datasets according to a $\textit{sensitivity profile}$ that is based on how much GNN performance changes due to a collection of graph perturbations. Our data-driven analysis provides a deeper understanding of which benchmarking data characteristics are leveraged by GNNs. Consequently, our taxonomy can aid in selection and development of adequate graph benchmarks, and better informed evaluation of future GNN methods. Finally, our approach and implementation in $\texttt{GTaxoGym}$ package are extendable to multiple graph prediction task types and future datasets.

翻译：神经网络(GNNs) 将神经网络的成功扩大到图形结构化数据,通过对其内在几何进行核算。虽然已经根据图表显示学习基准的收集情况,对开发GNN模型进行了广泛的研究,并取得了优异的性能,但目前还没有很好地了解该模型的哪些方面是由它们考察的。例如,它们在多大程度上测试了模型利用图形结构相对于节点特征的能力?在这里,我们制定了一个原则性的方法,根据$\textit{敏感度剖面值}对基准数据集进行分类,该方法的基础是由于收集了图表的扰动而使GNN的性能变化有多大。我们的数据驱动分析更深入地了解GNNs利用了哪些基准数据特征。因此,我们的分类学可以帮助选择和开发适当的图形基准,以及更知情地评估未来的GNN方法。最后,我们在$texttt{GTaxaxoGym} 组合中的做法和采用的方法可以扩展到多个图表预测任务类型和未来数据集。