Towards Multi-Grained Explainability for Graph Neural Networks. - Xiang Wang 010; Ying-Xin Wu 002; An Zhang 003; Xiangnan He 001; Tat-Seng Chua (NeurIPS, 2021)

Discovering Invariant Rationales for Graph Neural Networks. - Yingxin Wu 002; Xiang Wang 010; An Zhang 003; Xiangnan He 001; Tat-Seng Chua (ICLR, 2022)

Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. - Sihang Li 002; Xiang Wang 010; An Zhang 003; Yingxin Wu 002; Xiangnan He 001; Tat-Seng Chua (ICML, 2022)

Deconfounding to Explanation Evaluation in Graph Neural Networks. - Ying-Xin Wu 002; Xiang Wang 010; An Zhang 003; Xia Hu 001; Fuli Feng; Xiangnan He 001; Tat-Seng Chua (CoRR, 2022)

Reinforced Causal Explainer for Graph Neural Networks. - Xiang Wang 010; Yingxin Wu 002; An Zhang 003; Fuli Feng; Xiangnan He 001; Tat-Seng Chua (IEEE Trans. Pattern Anal. Mach. Intell., 2023)