Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
Authors: Weihuang Zheng, Jiashuo Liu, Jiaxing Li, Jiayun Wu, Peng Cui, Youyong Kong
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments: Experimental results on standard OOD node classification datasets demonstrate the effectiveness of the TAR framework, showing its superiority in addressing distributional shift problems. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University 2Department of Computer Science and Technology, Tsinghua University. Correspondence to: Youyong Kong <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Topology-Aware Dynamic Reweighting (TAR) Scheme |
| Open Source Code | No | The paper does not contain any explicit statement about releasing code or provide a link to a repository. The text in Appendix C mentions: "Our implementation is under the architecture of Py Torch (Paszke et al., 2019) and Py G (Fey & Lenssen, 2019)." This refers to third-party tools used, not the authors' own implementation code. |
| Open Datasets | Yes | We conduct experiments on five widely used node classification datasets from GOOD benchmark (Gui et al., 2022) to validate the effectiveness of TAR in improving out-of-distribution (OOD) generalization. We use five node classification datasets under both concept shift and covariate shift (the detailed definition of these two shift are provided in Appendix B) : Web KB (Pei et al., 2020), CBAS (Ying et al., 2019), Twitch (Rozemberczki & Sarkar, 2020), Cora (Bojchevski & G unnemann, 2017) and Arxiv (Hu et al., 2020). |
| Dataset Splits | Yes | We followed the GOOD benchmark (Gui et al., 2022) for data splitting, a standard widely adopted in prior research (Sui et al., 2023; Liu et al., 2023; Guo et al., 2024). |
| Hardware Specification | Yes | All of our experiments are run on one Ge Force RTX 3090 with 24GB. |
| Software Dependencies | Yes | The detailed versions of some key packages are listed below: python: 3.8 pytorch: 1.13.1 |
| Experiment Setup | Yes | For GCN, we configured the models with 3 layers, a hidden dimension of 300, a dropout rate of 0.5, and a learning rate of 0.01. For Polynormer, the layer of the local module is set to 5 and the global module is set to 1, with a hidden dimension of 512 and a learning rate of 0.001. Throughout all experiments, we employed the Adam optimizer with a weight decay of 0. The searching spaces for all the hyper-parameters of TAR are as follows. Entropy term β: {1, 0.1, 0.01, 0.001}. TAR inner learning rate γ: {0.1, 0.01, 0.001}. Gradient flow iterations Tin: {1, 3, 5, 10, 20}. Graph extrapolation ratio: {0.0, 0.2, 0.4} |