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

Reimagining GNN Explanations with ideas from Tabular Data