As post hoc explanations are increasingly used to understand the behavior of graph neural networks (GNNs), it becomes crucial to evaluate the quality and reliability of GNN explanations. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no or unreliable ground-truth explanations for a given task. Here, we introduce a synthetic graph data generator, ShapeGGen, which can generate a variety of benchmark datasets (e.g., varying graph sizes, degree distributions, homophilic vs. heterophilic graphs) accompanied by ground-truth explanations. Further, the flexibility to generate diverse synthetic datasets and corresponding ground-truth explanations allows us to mimic the data generated by various real-world applications. We include ShapeGGen and several real-world graph datasets into an open-source graph explainability library, GraphXAI. In addition to synthetic and real-world graph datasets with ground-truth explanations, GraphXAI provides data loaders, data processing functions, visualizers, GNN model implementations, and evaluation metrics to benchmark the performance of GNN explainability methods.

本文介绍了一种名称为ShapeGGen的数据生成器，它可以生成各种基准数据集，并配备了地面真实解释。ShapeGGen通过生成多样的综合数据集和相关地面真实解释，允许我们模仿各种现实应用程序生成的数据。ShapeGGen和几个真实世界的图形数据集被包括在一个开源的图解释库中，名为GraphXAI。除了具有地面真实解释的综合和真实世界的图形数据集外，GraphXAI还提供数据加载器，数据处理函数，可视化器，GNN模型实现和评估指标，以评估GNN可解释性方法的性能。

图神经网络的可解释性评估