Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Dissecting the Failure of Invariant Learning on Graphs
Authors: Qixun Wang, Yifei Wang, Yisen Wang, Xianghua Ying
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on graph OOD benchmarks validate the superiority of CIA and CIA-LRA, marking a significant advancement in nodelevel OOD generalization. ... 5 Experiments |
| Researcher Affiliation | Academia | Qixun Wang1 Yifei Wang 2 Yisen Wang 1,3 Xianghua Ying 1 1 State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University 2 CSAIL, MIT 3 Institute for Artificial Intelligence, Peking University |
| Pseudocode | Yes | Algorithm 1 Detailed Training Procedure of CIA-LRA |
| Open Source Code | Yes | The codes are available at https://github.com/NOVAglow646/Neur IPS24-Invariant-Learning-on-Graphs. |
| Open Datasets | Yes | We run experiments using 3-layer GAT and GCN on GOOD [Gui et al., 2022], a graph OOD benchmark. |
| Dataset Splits | Yes | Following Gui et al. [2022], we use an OOD validation set for model selection. |
| Hardware Specification | Yes | We use one NVIDIA Ge Force RTX 3090 or 4090 GPU for each single experiments. |
| Software Dependencies | No | The paper mentions using GAT and GCN models but does not specify versions for software dependencies like PyTorch, CUDA, or Python libraries. |
| Experiment Setup | Yes | The detailed experimental setup and hyperparameter settings are in Appendix C. |