| Graph structure learning with variational information bottleneck Q Sun, J Li, H Peng, J Wu, X Fu, C Ji, SY Philip Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4165-4174, 2022 | 140 | 2022 |
| Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing Q Sun, J Li, H Yuan, X Fu, H Peng, C Ji, Q Li, PS Yu Proceedings of the 31st ACM International Conference on Information …, 2022 | 39 | 2022 |
| ACE-HGNN: Adaptive curvature exploration hyperbolic graph neural network X Fu, J Li, J Wu, Q Sun, C Ji, S Wang, J Tan, H Peng, SY Philip 2021 IEEE international conference on data mining (ICDM), 111-120, 2021 | 30 | 2021 |
| Does graph distillation see like vision dataset counterpart? B Yang, K Wang, Q Sun, C Ji, X Fu, H Tang, Y You, J Li Advances in Neural Information Processing Systems 36, 2024 | 21 | 2024 |
| Hyperbolic geometric graph representation learning for hierarchy-imbalance node classification X Fu, Y Wei, Q Sun, H Yuan, J Wu, H Peng, J Li Proceedings of the ACM Web Conference 2023, 460-468, 2023 | 18 | 2023 |
| A robust and generalized framework for adversarial graph embedding J Li, X Fu, S Zhu, H Peng, S Wang, Q Sun, SY Philip, L He IEEE Transactions on Knowledge and Data Engineering 35 (11), 11004-11018, 2023 | 18 | 2023 |
| Heterogeneous graph neural network for privacy-preserving recommendation Y Wei, X Fu, Q Sun, H Peng, J Wu, J Wang, X Li 2022 IEEE International Conference on Data Mining (ICDM), 528-537, 2022 | 17 | 2022 |
| Curvature graph generative adversarial networks J Li, X Fu, Q Sun, C Ji, J Tan, J Wu, H Peng Proceedings of the ACM web conference 2022, 1528-1537, 2022 | 16 | 2022 |
| A three-phase approach to differentially private crucial patterns mining over data streams J Wang, C Liu, X Fu, X Luo, X Li Computers & Security 82, 30-48, 2019 | 13 | 2019 |
| Environment-aware dynamic graph learning for out-of-distribution generalization H Yuan, Q Sun, X Fu, Z Zhang, C Ji, H Peng, J Li Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| Higher-order memory guided temporal random walk for dynamic heterogeneous network embedding C Ji, T Zhao, Q Sun, X Fu, J Li Pattern Recognition 143, 109766, 2023 | 9 | 2023 |
| Self-organization preserved graph structure learning with principle of relevant information Q Sun, J Li, B Yang, X Fu, H Peng, SY Philip Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4643-4651, 2023 | 9 | 2023 |
| AIC-GNN: adversarial information completion for graph neural networks Q Wei, J Wang, X Fu, J Hu, X Li Information Sciences 626, 166-179, 2023 | 5 | 2023 |
| Unbiased and efficient self-supervised incremental contrastive learning C Ji, J Li, H Peng, J Wu, X Fu, Q Sun, PS Yu Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 4 | 2023 |
| Dynamic Graph Information Bottleneck H Yuan, Q Sun, X Fu, C Ji, J Li Proceedings of the ACM on Web Conference 2024, 469-480, 2024 | 3 | 2024 |
| Poincaré Differential Privacy for Hierarchy-aware Graph Embedding Y Wei, H Yuan, X Fu, Q Sun, H Peng, X Li, C Hu Proceedings of the AAAI Conference on Artificial Intelligence 38 (8), 9160-9168, 2024 | 3 | 2024 |
| GC-Bench: An Open and Unified Benchmark for Graph Condensation Q Sun, Z Chen, B Yang, C Ji, X Fu, S Zhou, H Peng, J Li, PS Yu arXiv preprint arXiv:2407.00615, 2024 | 1 | 2024 |
| IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning J Qin, H Yuan, Q Sun, L Xu, J Yuan, P Huang, Z Wang, X Fu, H Peng, J Li, ... arXiv preprint arXiv:2406.09870, 2024 | 1 | 2024 |
| Hyperbolic Geometric Latent Diffusion Model for Graph Generation X Fu, Y Gao, Y Wei, Q Sun, H Peng, J Li, X Li ICML, 2024 | 1 | 2024 |
| Heterogeneous graph neural network with semantic-aware differential privacy guarantees Y Wei, X Fu, D Yan, Q Sun, H Peng, J Wu, J Wang, X Li Knowledge and Information Systems 65 (10), 4085-4110, 2023 | 1 | 2023 |
Qingyun SunAssistant Professor, Beihang UniversityVerified email at act.buaa.edu.cn
