| Disentangled graph collaborative filtering X Wang, H Jin, A Zhang, X He, T Xu, TS Chua Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 485 | 2020 |
| Discovering invariant rationales for graph neural networks YX Wu, X Wang, A Zhang, X He, TS Chua arXiv preprint arXiv:2201.12872, 2022 | 214 | 2022 |
| Let invariant rationale discovery inspire graph contrastive learning S Li, X Wang, A Zhang, Y Wu, X He, TS Chua International conference on machine learning, 13052-13065, 2022 | 94 | 2022 |
| Towards multi-grained explainability for graph neural networks X Wang, Y Wu, A Zhang, X He, TS Chua Advances in Neural Information Processing Systems 34, 18446-18458, 2021 | 76 | 2021 |
| Crosscbr: Cross-view contrastive learning for bundle recommendation Y Ma, Y He, A Zhang, X Wang, TS Chua Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 67 | 2022 |
| On generative agents in recommendation A Zhang, Y Chen, L Sheng, X Wang, TS Chua Proceedings of the 47th International ACM SIGIR Conference on Research and …, 2024 | 46 | 2024 |
| Reinforced causal explainer for graph neural networks X Wang, Y Wu, A Zhang, F Feng, X He, TS Chua IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 2297-2309, 2022 | 44 | 2022 |
| Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering A Zhang, W Ma, X Wang, TS Chua Thirty-sixth Conference on Neural Information Processing Systems, 2022 | 42 | 2022 |
| Invariant Collaborative Filtering to Popularity Distribution Shift A Zhang, J Zheng, X Wang, Y Yuan, TS ChuI arXiv preprint arXiv:2302.05328, 2023 | 29 | 2023 |
| Large language model can interpret latent space of sequential recommender Z Yang, J Wu, Y Luo, J Zhang, Y Yuan, A Zhang, X Wang, X He arXiv preprint arXiv:2310.20487, 2023 | 24 | 2023 |
| Cooperative explanations of graph neural networks J Fang, X Wang, A Zhang, Z Liu, X He, TS Chua Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 22 | 2023 |
| Causal screening to interpret graph neural networks X Wang, Y Wu, A Zhang, X He, T Chua | 19* | 2021 |
| Evaluating post-hoc explanations for graph neural networks via robustness analysis J Fang, W Liu, Y Gao, Z Liu, A Zhang, X Wang, X He Advances in Neural Information Processing Systems 36, 2024 | 17 | 2024 |
| Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting A Zhang, F Liu, W Ma, Z Cai, X Wang, T Chua Eleventh International Conference on Learning Representations, 2023 | 14 | 2023 |
| Deconfounding to explanation evaluation in graph neural networks YX Wu, X Wang, A Zhang, X Hu, F Feng, X He, TS Chua arXiv preprint arXiv:2201.08802, 2022 | 14 | 2022 |
| Relm: Leveraging language models for enhanced chemical reaction prediction Y Shi, A Zhang, E Zhang, Z Liu, X Wang arXiv preprint arXiv:2310.13590, 2023 | 13 | 2023 |
| Adversarial causal augmentation for graph covariate shift Y Sui, X Wang, J Wu, A Zhang, X He arXiv preprint arXiv:2211.02843, 2022 | 10 | 2022 |
| Empowering collaborative filtering with principled adversarial contrastive loss A Zhang, L Sheng, Z Cai, X Wang, TS Chua Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| Online distillation-enhanced multi-modal transformer for sequential recommendation W Ji, X Liu, A Zhang, Y Wei, Y Ni, X Wang Proceedings of the 31st ACM International Conference on Multimedia, 955-965, 2023 | 9 | 2023 |
| On regularization for explaining graph neural networks: An information theory perspective J Fang, G Zhang, K Wang, W Du, Y Duan, Y Wu, R Zimmermann, X Chu, ... IEEE Transactions on Knowledge and Data Engineering, 2024 | 8 | 2024 |
Xiang WangUniversity of Science and Technology of ChinaVerified email at ustc.edu.cn
