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Reimagining GNN Explanations with ideas from Tabular Data

06/23/2021

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by Anjali Singh, et al.

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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.

Anjali Singh
2 publications
Shamanth R Nayak K
2 publications
Balaji Ganesan
10 publications

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