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.
翻译:由于越来越多地使用事后临时解释来理解图形神经网络(GNN)的行为,评估GNN解释的质量和可靠性变得至关重要。然而,评估GNN解释的质量具有挑战性,因为现有的图表数据集没有或不可靠的地面真实解释某项任务。在这里,我们引入了一个合成图形数据生成器,ShapeGGGen, 它可以产生各种基准数据集(例如,不同的图形大小、度分布、同性对异性嗜血性图),并辅以地面真相解释。此外,生成各种合成数据集的灵活性和相应的地面真相解释使我们能够模拟各种现实应用生成的数据。我们把ShapeGGGen和几个真实世界图形数据集纳入一个开放源图形解释图书馆,GreagXAI。除了提供带有地面真相解释的合成和真实世界图形数据集外,GreagXAI还提供数据装载器、数据处理功能、视觉化器、GNNN模型实施和评估指标,以衡量GNN的解释方法的性。