| Rethinking propagation for unsupervised graph domain adaptation M Liu, Z Fang, Z Zhang, M Gu, S Zhou, X Wang, J Bu Proceedings of the AAAI Conference on Artificial Intelligence 38 (12), 13963 …, 2024 | 17 | 2024 |
| Homophily-enhanced Structure Learning for Graph Clustering M Gu, G Yang, S Zhou, N Ma, J Chen, Q Tan, M Liu, J Bu Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 13 | 2023 |
| Revisiting the message passing in heterophilous graph neural networks Z Zheng, Y Bei, S Zhou, Y Ma, M Gu, H Xu, C Lai, J Chen, J Bu arXiv preprint arXiv:2405.17768, 2024 | 3 | 2024 |
| Structure enhanced prototypical alignment for unsupervised cross-domain node classification M Liu, Z Zhang, N Ma, M Gu, H Wang, S Zhou, J Bu Neural Networks 177, 106396, 2024 | 2 | 2024 |
| Heterophilous distribution propagation for Graph Neural Networks Z Zheng, S Zhou, H Xu, M Gu, Y Xu, A Li, Y Li, J Gu, J Bu Neural Networks 184, 107014, 2025 | | 2025 |
| Universal Inceptive GNNs by Eliminating the Smoothness-generalization Dilemma M Gu, Z Zheng, S Zhou, M Liu, J Chen, T Qiao, L Li, J Bu arXiv preprint arXiv:2412.09805, 2024 | | 2024 |
| Towards a Unified Framework of Clustering-based Anomaly Detection Z Fang, M Gu, S Zhou, J Chen, Q Tan, H Wang, J Bu arXiv preprint arXiv:2406.00452, 2024 | | 2024 |
| Frequency Self-Adaptation Graph Neural Network for Unsupervised Graph Anomaly Detection M Gu, G Yang, Z Zheng, M Liu, H Wang, J Chen, S Zhou, J Bu Available at SSRN 5182010, 0 | | |
| Exploring and Unleashing the Power of Message Passing on Heterophilous Graphs Z Zheng, Y Bei, S Zhou, Y Ma, M Gu, H XU, C Lai, J Chen, J Bu | | |
