1 code implementation • 9 Feb 2024 • Haonan Yuan, Qingyun Sun, Xingcheng Fu, Cheng Ji, JianXin Li
Leveraged by the Information Bottleneck (IB) principle, we first propose the expected optimal representations should satisfy the Minimal-Sufficient-Consensual (MSC) Condition.
no code implementations • 19 Dec 2023 • Yuecen Wei, Haonan Yuan, Xingcheng Fu, Qingyun Sun, Hao Peng, Xianxian Li, Chunming Hu
Specifically, PoinDP first learns the hierarchy weights for each entity based on the Poincar\'e model in hyperbolic space.
1 code implementation • NeurIPS 2023 • Haonan Yuan, Qingyun Sun, Xingcheng Fu, Ziwei Zhang, Cheng Ji, Hao Peng, JianXin Li
To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.
1 code implementation • 11 Apr 2023 • Xingcheng Fu, Yuecen Wei, Qingyun Sun, Haonan Yuan, Jia Wu, Hao Peng, JianXin Li
We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments.
1 code implementation • 17 Aug 2022 • Qingyun Sun, JianXin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, Philip S. Yu
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.