Hierarchical graph convolutional networks for semi-supervised node classification F Hu, Y Zhu, S Wu, L Wang, T Tan Proceedings of the Twenty-Eighth International Joint Conference on …, 0 | 158* | |
Graphair: Graph representation learning with neighborhood aggregation and interaction F Hu, Y Zhu, S Wu, W Huang, L Wang, T Tan Pattern Recognition 112, 107745, 2021 | 42 | 2021 |
Fully hyperbolic graph convolution network for recommendation L Wang, F Hu, S Wu, L Wang Proceedings of the 30th ACM International Conference on Information …, 2021 | 18 | 2021 |
GraphDIVE: Graph Classification by Mixture of Diverse Experts. F Hu, L Wang, Q Liu, S Wu, L Wang, T Tan IJCAI, 2080-2086, 2022 | 15* | 2022 |
Semi-supervised node classification via hierarchical graph convolutional networks F Hu, Y Zhu, S Wu, L Wang, T Tan arXiv preprint arXiv:1902.06667, 2019 | 7 | 2019 |
Label-informed graph structure learning for node classification L Wang, F Hu, S Wu, L Wang Proceedings of the 30th ACM International Conference on Information …, 2021 | 1 | 2021 |
Second-Order Global Attention Networks for Graph Classification and Regression F Hu, Z Cui, S Wu, Q Liu, J Wu, L Wang, T Tan CAAI International Conference on Artificial Intelligence, 496-507, 2022 | | 2022 |