Publication
ICML 2021
Workshop paper

Reimagining GNN Explanations with ideas from Tabular Data

ICML 2021
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Abstract

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.

Date

18 Jul 2021

Publication

ICML 2021

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