2021
Robust Counterfactual Explanations on Graph Neural Networks
NeurIPS 2021poster
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition. Most existing methods generate explanations by identifying a subgraph of an input graph that has a strong correlation…