[KDD 2025] Track and Tweak: Monitoring and Improving Group Fairness for Temporal Graph Neural Networks in Real Time.
Z Song, M Li, Y Zhang, I King, JM Hernández-Lobato
[ICML 2025] Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification.
L Liang, F Bu, Z Song, Z Xu, S Pan, K Shin
[ICML 2025] Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space.
Z Song, Z Meng, JM Hernández-Lobato
[TheWebConf 2025] FedEDM: Federated Equivariant Diffusion Model for 3D Molecule Generation with Enhanced Communication Efficiency.
Z Song, I King, JM Hernández-Lobato
[AAAI 2025] Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning.
M Li, C Yang, C Xu, Z Song, X Jiang, J Guo, H Leung, I King
[KDD 2024] Geometric view of soft decorrelation in self-supervised learning.
Y Zhang, H Zhu, Z Song, Y Chen, X Fu, Z Meng, P Koniusz, I King
[IJCAI 2024] A Systematic Survey on Federated Semi-supervised Learning.
Z Song, X Yang, Y Zhang, X Fu, Z Xu, I King
[IJCAI 2024] Towards Geometric Normalization Techniques in SE(3) Equivariant Graph Neural Networks for Physical Dynamics Simulations.
Z Meng, L Zeng, Z Song, T Xu, P Zhao, I King
[KDD 2023] Contrastive Cross-scale Graph Knowledge Synergy.
Y Zhang, Y Chen, Z Song, I King
[NeurIPS 2023] No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning.
Z Song, Y Zhang, I King
[NeurIPS 2023] Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective.
Y Zhang, H Zhu, Z Song, P Koniusz, I King
[NeurIPS 2023] Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning.
Z Song, Y Zhang, I King
[NeurIPS 2023] Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily.
L Liang, X Hu, Z Xu, Z Song, I King
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