Graph structure learning with variational information bottleneck Q Sun, J Li, H Peng, J Wu, X Fu, C Ji, PS Yu Proceedings of the AAAI conference on artificial intelligence 36 (4), 4165-4174, 2022 | 181 | 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 | 51 | 2022 |
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, 53201-53226, 2023 | 39 | 2023 |
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, PS Yu 2021 IEEE international conference on data mining (ICDM), 111-120, 2021 | 34 | 2021 |
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 | 31 | 2023 |
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, 49715-49747, 2023 | 23 | 2023 |
A robust and generalized framework for adversarial graph embedding J Li, X Fu, S Zhu, H Peng, S Wang, Q Sun, PS Yu, L He IEEE Transactions on Knowledge and Data Engineering 35 (11), 11004-11018, 2023 | 21 | 2023 |
Dynamic graph information bottleneck H Yuan, Q Sun, X Fu, C Ji, J Li Proceedings of the ACM Web Conference 2024, 469-480, 2024 | 19 | 2024 |
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 | 19 | 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 | 17 | 2022 |
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 | 13 | 2023 |
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 |
Self-organization preserved graph structure learning with principle of relevant information Q Sun, J Li, B Yang, X Fu, H Peng, PS Yu Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4643-4651, 2023 | 12 | 2023 |
Hyperbolic Geometric Latent Diffusion Model for Graph Generation X Fu, Y Gao, Y Wei, Q Sun, H Peng, J Li, X Li Forty-first International Conference on Machine Learning. ICML 2024., 2024 | 11 | 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 | 9 | 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 The 38-th Annual Conference on Neural Information Processing Systems …, 2024 | 8 | 2024 |
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 | 7 | 2023 |
ReGCL: rethinking message passing in graph contrastive learning C Ji, Z Huang, Q Sun, H Peng, X Fu, Q Li, J Li Proceedings of the AAAI Conference on Artificial Intelligence 38 (8), 8544-8552, 2024 | 5 | 2024 |
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 |
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 | 3 | 2024 |