| 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 | 7 | 2023 |
| 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 | 4 | 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 |
| 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 | 1 | 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 |
| 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 arXiv preprint arXiv:2405.20640, 2024 | | 2024 |
