  Explainability techniques for Graph Neural Networks still have a long way to
go compared to explanations available for both neural and decision decision
tree-based models trained on tabular data. Using a task that straddles both
graphs and tabular data, namely Entity Matching, we comment on key aspects of
explainability that are missing in GNN model explanations.
