Are Graph Neural Network Explainers Robust to Graph Noises? - Yiqiao Li 001; Sunny Verma; Shuiqiao Yang; Jianlong Zhou; Fang Chen 001 (AI, 2022)

A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics. - Yiqiao Li 001; Jianlong Zhou; Sunny Verma; Fang Chen 001 (CoRR, 2022)

Impact of Fidelity and Robustness of Machine Learning Explanations on User Trust. - Bo Wang; Jianlong Zhou; Yiqiao Li 001; Fang Chen 001 (AI (2), 2023)

ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks. - Yiqiao Li 001; Jianlong Zhou; Yifei Dong; Niusha Shafiabady; Fang Chen 001 (CIKM, 2023)

GANExplainer: GAN-based Graph Neural Networks Explainer. - Yiqiao Li 001; Jianlong Zhou; Boyuan Zheng; Fang Chen 001 (CoRR, 2023)

Explaining Imitation Learning Through Frames. - Boyuan Zheng; Jianlong Zhou; Chunjie Liu; Yiqiao Li 001; Fang Chen 001 (IEEE Intell. Syst., 2024)

Reliable and Faithful Generative Explainers for Graph Neural Networks. - Yiqiao Li 001; Jianlong Zhou; Boyuan Zheng; Niusha Shafiabady; Fang Chen 001 (Mach. Learn. Knowl. Extr., 2024)

Can LLM Assist in the Evaluation of the Quality of Machine Learning Explanations? - Bo Wang; Yiqiao Li 001; Jianlong Zhou; Fang Chen 001 (CoRR, 2025)