| Multi-scale contrastive siamese networks for self-supervised graph representation learning M Jin, Y Zheng, YF Li, C Gong, C Zhou, S Pan arXiv preprint arXiv:2105.05682, 2021 | 210 | 2021 |
| Large language models for scientific discovery in molecular property prediction Y Zheng, HY Koh, J Ju, ATN Nguyen, LT May, GI Webb, S Pan Nature Machine Intelligence, 1-11, 2025 | 149* | 2025 |
| Rethinking and scaling up graph contrastive learning: An extremely efficient approach with group discrimination Y Zheng, S Pan, V Lee, Y Zheng, PS Yu Advances in Neural Information Processing Systems 35, 10809-10820, 2022 | 149 | 2022 |
| Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating Y Liu, Y Zheng, D Zhang, VCS Lee, S Pan Proceedings of the AAAI conference on artificial intelligence 37 (4), 4516-4524, 2023 | 136 | 2023 |
| A survey on fairness-aware recommender systems D Jin, L Wang, H Zhang, Y Zheng, W Ding, F Xia, S Pan Information Fusion 100, 101906, 2023 | 98 | 2023 |
| Finding the missing-half: Graph complementary learning for homophily-prone and heterophily-prone graphs Y Zheng, H Zhang, V Lee, Y Zheng, X Wang, S Pan International Conference on Machine Learning, 42492-42505, 2023 | 70 | 2023 |
| Large language models for drug discovery and development Y Zheng, HY Koh, J Ju, M Yang, LT May, GI Webb, L Li, S Pan, G Church Patterns 6 (10), 2025 | 62* | 2025 |
| Dual intent enhanced graph neural network for session-based new item recommendation D Jin, L Wang, Y Zheng, G Song, F Jiang, X Li, W Lin, S Pan Proceedings of the ACM web conference 2023, 684-693, 2023 | 55 | 2023 |
| Prem: A simple yet effective approach for node-level graph anomaly detection J Pan*, Y Liu*, Y Zheng*, S Pan 2023 IEEE International Conference on Data Mining (ICDM), 1253-1258, 2023 | 45 | 2023 |
| Heterogeneous graph attention network for small and medium-sized enterprises bankruptcy prediction Y Zheng, VCS Lee, Z Wu, S Pan Pacific-Asia Conference on Knowledge Discovery and Data Mining, 140-151, 2021 | 45 | 2021 |
| Integrating graphs with large language models: Methods and prospects S Pan, Y Zheng, Y Liu IEEE Intelligent Systems 39 (1), 64-68, 2024 | 41 | 2024 |
| Contrastive graph similarity networks L Wang*, Y Zheng*, D Jin, F Li, Y Qiao, S Pan ACM Transactions on the Web 18 (2), 1-20, 2024 | 37 | 2024 |
| Toward graph self-supervised learning with contrastive adjusted zooming Y Zheng, M Jin, S Pan, YF Li, H Peng, M Li, Z Li IEEE Transactions on Neural Networks and Learning Systems 35 (7), 8882-8896, 2022 | 35 | 2022 |
| CGMN: A contrastive graph matching network for self-supervised graph similarity learning D Jin, L Wang, Y Zheng, X Li, F Jiang, W Lin, S Pan arXiv preprint arXiv:2205.15083, 2022 | 35 | 2022 |
| Improving augmentation consistency for graph contrastive learning W Bu, X Cao, Y Zheng, S Pan Pattern Recognition 148, 110182, 2024 | 27 | 2024 |
| Unifying graph contrastive learning with flexible contextual scopes Y Zheng, Y Zheng, X Zhou, C Gong, VCS Lee, S Pan 2022 IEEE International Conference on Data Mining (ICDM), 793-802, 2022 | 26 | 2022 |
| A label-free heterophily-guided approach for unsupervised graph fraud detection J Pan, Y Liu, X Zheng, Y Zheng, AWC Liew, F Li, S Pan Proceedings of the AAAI Conference on Artificial Intelligence 39 (12), 12443 …, 2025 | 20 | 2025 |
| Collaborative expert llms guided multi-objective molecular optimization J Yu*, Y Zheng*, HY Koh*, S Pan, T Wang, H Wang arXiv preprint arXiv:2503.03503, 2025 | 15 | 2025 |
| Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective Y Tao, K Guo, Y Zheng, S Pan, X Cao, Y Chang Information Sciences 657, 119952, 2024 | 13 | 2024 |
| AI-driven protein design HY Koh*, Y Zheng*, M Yang, R Arora, GI Webb, S Pan, L Li, GM Church Nature Reviews Bioengineering, 1-23, 2025 | 12 | 2025 |
Shirui PanProfessor, ARC Future Fellow, FQA, Director of TrustAGI Lab, Griffith UniversityVerified email at griffith.edu.au
