| Beyond low-frequency information in graph convolutional networks D Bo, X Wang, C Shi, H Shen Proceedings of the AAAI conference on artificial intelligence 35 (5), 3950-3957, 2021 | 420 | 2021 |
| Am-gcn: Adaptive multi-channel graph convolutional networks X Wang, M Zhu, D Bo, P Cui, C Shi, J Pei Proceedings of the 26th ACM SIGKDD International conference on knowledge …, 2020 | 410 | 2020 |
| Structural deep clustering network D Bo, X Wang, C Shi, M Zhu, E Lu, P Cui Proceedings of the web conference 2020, 1400-1410, 2020 | 410 | 2020 |
| A survey on heterogeneous graph embedding: methods, techniques, applications and sources X Wang, D Bo, C Shi, S Fan, Y Ye, SY Philip IEEE Transactions on Big Data 9 (2), 415-436, 2022 | 249 | 2022 |
| Specformer: Spectral Graph Neural Networks Meet Transformers D Bo, C Shi, L Wang, R Liao The Eleventh International Conference on Learning Representations, 2023 | 43 | 2023 |
| Revisiting graph contrastive learning from the perspective of graph spectrum N Liu, X Wang, D Bo, C Shi, J Pei Advances in Neural Information Processing Systems 35, 2972-2983, 2022 | 35 | 2022 |
| Regularizing graph neural networks via consistency-diversity graph augmentations D Bo, BB Hu, X Wang, Z Zhang, C Shi, J Zhou Proceedings of the aaai conference on artificial intelligence 36 (4), 3913-3921, 2022 | 20 | 2022 |
| A survey on spectral graph neural networks D Bo, X Wang, Y Liu, Y Fang, Y Li, C Shi arXiv preprint arXiv:2302.05631, 2023 | 16 | 2023 |
| Graph contrastive learning with stable and scalable spectral encoding D Bo, Y Fang, Y Liu, C Shi Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Graph Condensation via Eigenbasis Matching Y Liu, D Bo, C Shi arXiv preprint arXiv:2310.09202, 2023 | 2 | 2023 |
