Grace Guo, Lifu Deng, et al.
FAccT 2024
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.
Grace Guo, Lifu Deng, et al.
FAccT 2024
Taku Ito, Luca Cocchi, et al.
ICML 2025
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
Yuan Cai, Jasmina Burek, et al.
ICML 2021