Reimagining GNN Explanations with ideas from Tabular Data
Autor: | Singh, Anjali, K, Shamanth R Nayak, Ganesan, Balaji |
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Rok vydání: | 2021 |
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Druh dokumentu: | Working Paper |
Popis: | 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. Comment: 4 pages, 8 figures, XAI Workshop at ICML 2021 |
Databáze: | arXiv |
Externí odkaz: |