| -Laplacian Based Graph Neural Networks G Fu, P Zhao, Y Bian International Conference on Machine Learning, ICML 2022, 6878-6917, 2022 | 38 | 2022 |
| Learning topological representation for networks via hierarchical sampling G Fu, C Hou, X Yao 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 31 | 2019 |
| Representation Learning for Heterogeneous Information Networks via Embedding Events G Fu, B Yuan, Q Duan, X Yao International Conference on Neural Information Processing, 327-339, 2019 | 27 | 2019 |
| Understanding Graph Neural Networks from Graph Signal Denoising Perspectives G Fu, Y Hou, J Zhang, K Ma, BF Kamhoua, J Cheng arXiv preprint arXiv:2006.04386, 2020 | 23 | 2020 |
| Fairness-aware Message Passing for Graph Neural Networks H Zhu, G Fu, Z Guo, Z Zhang, T Xiao, S Wang arXiv preprint arXiv:2306.11132, 2023 | 10 | 2023 |
| What has been Enhanced in my Knowledge-Enhanced Language Model? Y Hou, G Fu, M Sachan EMNLP 2022, 2022 | 8* | 2022 |
| Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing G Fu, MH Dupty, Y Dong, LW Sun arXiv preprint arXiv:2308.03306, 2023 | 5* | 2023 |
