| A survey of explainable graph neural networks: Taxonomy and evaluation metrics Y Li, J Zhou, S Verma, F Chen arXiv preprint arXiv:2207.12599, 2022 | 51 | 2022 |
| Impact of Fidelity and Robustness of Machine Learning Explanations on User Trust B Wang, J Zhou, Y Li, F Chen Australasian Joint Conference on Artificial Intelligence, 209-220, 2023 | 5 | 2023 |
| Are Graph Neural Network Explainers Robust to Graph Noises? Y Li, S Verma, S Yang, J Zhou, F Chen Australasian Joint Conference on Artificial Intelligence, 161-174, 2022 | 5 | 2022 |
| NE-UserCF: Collaborative filtering recommender system model based on NMF and E2LSH Y Wu, Y Li, R Qian International Journal of Performability Engineering 13 (5), 610, 2017 | 5 | 2017 |
| GANExplainer: GAN-based Graph Neural Networks Explainer Y Li, J Zhou, B Zheng, F Chen arXiv preprint arXiv:2301.00012, 2022 | 4 | 2022 |
| ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks Y Li, J Zhou, Y Dong, N Shafiabady, F Chen Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 2 | 2023 |
| Design and implementation of distributed session based on Beansdb Y Li, Y Wu, X Duan 2017 2nd IEEE International Conference on Computational Intelligence and …, 2017 | 2 | 2017 |
| Reliable and Faithful Generative Explainers for Graph Neural Networks Y Li, J Zhou, B Zheng, N Shafiabady, F Chen Machine Learning and Knowledge Extraction 6 (4), 2913-2929, 2024 | | 2024 |
| Explaining Imitation Learning Through Frames B Zheng, J Zhou, C Liu, Y Li, F Chen IEEE Intelligent Systems, 2024 | | 2024 |
| ICFLSB: An Improved Collaborative Filtering Algorithm based on Latent Semantic and Bayesian Y Wu, R Qian, X Dong, Y Li, X Niu International Journal of Performability Engineering 14 (1), 26, 2018 | | 2018 |
| An Automatic Simulation Framework to Find Loopholes in Regimes Y Wu, Y Li International Journal of Performability Engineering 13 (5), 670, 2017 | | 2017 |
