| Fairness in graph mining: A survey Y Dong, J Ma, S Wang, C Chen, J Li IEEE Transactions on Knowledge and Data Engineering 35 (10), 10583-10602, 2023 | 129 | 2023 |
| Knowledge Editing for Large Language Models: A Survey S Wang, Y Zhu, H Liu, Z Zheng, C Chen, J Li ACM Computing Surveys, 2023 | 84 | 2023 |
| Large language models for data annotation: A survey Z Tan, A Beigi, S Wang, R Guo, A Bhattacharjee, B Jiang, M Karami, J Li, ... EMNLP Main, 2024 | 77 | 2024 |
| Reform: Error-aware few-shot knowledge graph completion S Wang, X Huang, C Chen, L Wu, J Li Proceedings of the 30th ACM international conference on information …, 2021 | 56 | 2021 |
| Hierarchical heterogeneous graph representation learning for short text classification Y Wang, S Wang, Q Yao, D Dou EMNLP, 2021 | 44 | 2021 |
| Task-adaptive few-shot node classification S Wang, K Ding, C Zhang, C Chen, J Li Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 42 | 2022 |
| On structural explanation of bias in graph neural networks Y Dong, S Wang, Y Wang, T Derr, J Li Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 35 | 2022 |
| Interpreting unfairness in graph neural networks via training node attribution Y Dong, S Wang, J Ma, N Liu, J Li Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7441-7449, 2023 | 31 | 2023 |
| Faith: Few-shot graph classification with hierarchical task graphs S Wang, Y Dong, X Huang, C Chen, J Li IJCAI, 2022 | 22 | 2022 |
| Federated few-shot learning S Wang, X Fu, K Ding, C Chen, H Chen, J Li Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 21 | 2023 |
| Transductive linear probing: A novel framework for few-shot node classification Z Tan, S Wang, K Ding, J Li, H Liu Learning on Graphs Conference, 4: 1-4: 21, 2022 | 19 | 2022 |
| Interpreting pretrained language models via concept bottlenecks Z Tan, L Cheng, S Wang, B Yuan, J Li, H Liu Pacific-Asia Conference on Knowledge Discovery and Data Mining, 56-74, 2024 | 16 | 2024 |
| Noise-robust fine-tuning of pretrained language models via external guidance S Wang, Z Tan, R Guo, J Li EMNLP Findings, 2023 | 15 | 2023 |
| Few-shot node classification with extremely weak supervision S Wang, Y Dong, K Ding, C Chen, J Li Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 15 | 2023 |
| Graph few-shot learning with task-specific structures S Wang, C Chen, J Li Advances in Neural Information Processing Systems 35, 38925-38936, 2022 | 15 | 2022 |
| Recognizing medical search query intent by few-shot learning Y Wang, S Wang, Y Li, D Dou Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 15 | 2022 |
| Label-guided learning for text classification X Liu, S Wang, X Zhang, X You, J Wu, D Dou arXiv preprint arXiv:2002.10772, 2020 | 11 | 2020 |
| Contrastive meta-learning for few-shot node classification S Wang, Z Tan, H Liu, J Li Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 8 | 2023 |
| Ceb: Compositional evaluation benchmark for fairness in large language models S Wang, P Wang, T Zhou, Y Dong, Z Tan, J Li arXiv preprint arXiv:2407.02408, 2024 | 6 | 2024 |
| Safety in Graph Machine Learning: Threats and Safeguards S Wang, Y Dong, B Zhang, Z Chen, X Fu, Y He, C Shen, C Zhang, ... arXiv preprint arXiv:2405.11034, 2024 | 5 | 2024 |
Jundong LiAssistant Professor, University of VirginiaVerified email at virginia.edu
