| Edits: Modeling and mitigating data bias for graph neural networks Y Dong, N Liu, B Jalaian, J Li Proceedings of the ACM web conference 2022, 1259-1269, 2022 | 100 | 2022 |
| Individual fairness for graph neural networks: A ranking based approach Y Dong, J Kang, H Tong, J Li Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 99 | 2021 |
| Fairness in graph mining: A survey Y Dong, J Ma, S Wang, C Chen, J Li IEEE Transactions on Knowledge and Data Engineering, 2023 | 81 | 2023 |
| Improving fairness in graph neural networks via mitigating sensitive attribute leakage Y Wang, Y Zhao, Y Dong, H Chen, J Li, T Derr Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and …, 2022 | 62 | 2022 |
| Adagnn: Graph neural networks with adaptive frequency response filter Y Dong, K Ding, B Jalaian, S Ji, J Li Proceedings of the 30th ACM international conference on information …, 2021 | 48 | 2021 |
| Federated graph machine learning: A survey of concepts, techniques, and applications X Fu, B Zhang, Y Dong, C Chen, J Li ACM SIGKDD Explorations Newsletter 24 (2), 32-47, 2022 | 38 | 2022 |
| Contrastive attributed network anomaly detection with data augmentation Z Xu, X Huang, Y Zhao, Y Dong, J Li Pacific-Asia Conference on Knowledge Discovery and Data Mining, 444-457, 2022 | 35 | 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 | 29 | 2022 |
| Guide: Group equality informed individual fairness in graph neural networks W Song, Y Dong, N Liu, J Li Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 24 | 2022 |
| Assessing the causal impact of COVID-19 related policies on outbreak dynamics: A case study in the US J Ma, Y Dong, Z Huang, D Mietchen, J Li Proceedings of the ACM Web Conference 2022, 2678-2686, 2022 | 23 | 2022 |
| Forecasting pavement performance with a feature fusion LSTM-BPNN model Y Dong, Y Shao, X Li, S Li, L Quan, W Zhang, J Du Proceedings of the 28th ACM international conference on information and …, 2019 | 21 | 2019 |
| 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 | 20 | 2023 |
| Empowering next poi recommendation with multi-relational modeling Z Huang, J Ma, Y Dong, NZ Foutz, J Li Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 15 | 2022 |
| Faith: Few-shot graph classification with hierarchical task graphs S Wang, Y Dong, X Huang, C Chen, J Li arXiv preprint arXiv:2205.02435, 2022 | 15 | 2022 |
| Reliant: Fair knowledge distillation for graph neural networks Y Dong, B Zhang, Y Yuan, N Zou, Q Wang, J Li Proceedings of the 2023 SIAM International Conference on Data Mining (SDM …, 2023 | 11 | 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 | 8 | 2023 |
| Gigamae: Generalizable graph masked autoencoder via collaborative latent space reconstruction Y Shi, Y Dong, Q Tan, J Li, N Liu Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 5 | 2023 |
| Fairness in Graph Machine Learning: Recent Advances and Future Prospectives Y Dong, OD Kose, Y Shen, J Li Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 2 | 2023 |
| Empower Post-hoc Graph Explanations with Information Bottleneck: A Pre-training and Fine-tuning Perspective J Wang, M Luo, J Li, Y Lin, Y Dong, JS Dong, Q Zheng Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 2 | 2023 |
| When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback? Y Dong, J Li, T Schnabel Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 2 | 2023 |
Jundong LiAssistant Professor, University of VirginiaDirección de correo verificada de virginia.edu
