Scaling Up Graph Neural Networks Via Graph Coarsening Z Huang, S Zhang, C Xi, T Liu, M Zhou Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 75 | 2021 |
Effective stabilized self-training on few-labeled graph data Z Zhou, J Shi, S Zhang, Z Huang, Q Li Information Sciences 631, 369-384, 2023 | 31* | 2023 |
SCE: Scalable Network Embedding from Sparsest Cut S Zhang, Z Huang, H Zhou, Z Zhou Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2020 | 15 | 2020 |
Few-shot classification on graphs with structural regularized GCNs S Zhang, Z Zhou, Z Huang, Z Wei | 14 | 2018 |
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction B Li, M Zhou, S Zhang, M Yang, D Lian, Z Huang Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 7 | 2022 |
StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning S Zhang, W Yang, X Cao, H Zhang, Z Huang ICLR 2024, 2023 | 1 | 2023 |
UNREAL: Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification L Yan, S Zhang, B Li, M Zhou, Z Huang arXiv preprint arXiv:2303.10371, 2023 | 1 | 2023 |
Enhancing Performance of Coarsened Graphs with Gradient-Matching W Yang, S Zhang, Z Huang ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | | 2024 |
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition D Yan, G Wei, C Yang, S Zhang Advances in Neural Information Processing Systems 36, 2024 | | 2024 |