  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.
