2023
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
ICML 2023poster
The success of graph neural networks (GNNs) provokes the question about explainability: ``Which fraction of the input graph is the most determinant of the prediction?'' Particularly, parametric explainers prevail in existing approaches because of their more robust capability to decipher the black-bo…