59 citations · 66 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 7 cited
Deconfounding to Explanation Evaluation in Graph Neural Networks
Ying-Xin Wu, Xiang Wang, An Zhang +4
Explainability of graph neural networks (GNNs) aims to answer "Why the GNN made a certain prediction?", which is crucial to interpret the model prediction. The feature attribution…
cs.LG2022★ 59 cited
Discovering Invariant Rationales for Graph Neural Networks
Ying-Xin Wu, Xiang Wang, An Zhang +2
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction. Unfortunately,…