Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport
Authors: Jiying Zhang, Xi Xiao, Long-Kai Huang, Yu Rong, Yatao Bian
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate GTOT-Tuning on eight downstream tasks with various GNN backbones1 and demonstrate that it achieves state-of-the-art fine-tuning performance for GNNs. The experiments conducted on eight different datasets with various GNN backbones show that GTOT-Tuning achieves the best performance among all baselines, validating the effectiveness and generalization ability of our method. |
| Researcher Affiliation | Collaboration | Jiying Zhang1,2 , Xi Xiao2 , Long-Kai Huang1 , Yu Rong1 and Yatao Bian1 1Tencent AI Lab 2Shenzhen International Graduate School, Tsinghua University |
| Pseudocode | Yes | Algorithm 1 Computing Masked Wasserstein Distance |
| Open Source Code | Yes | Code: https://github.com/youjibiying/GTOT-Tuning. |
| Open Datasets | Yes | In addition, eight binary classification datasets in Molecule Net [Wu et al., 2018] serve as downstream tasks for evaluating the finetuning strategies, where the scaffold split scheme is used for dataset split. |
| Dataset Splits | No | The paper mentions that 'the scaffold split scheme is used for dataset split' for MoleculeNet, but it does not provide specific quantitative details like percentages or sample counts for training, validation, or test sets in the main body. It refers to an appendix for more details, which is not available. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory details) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9', 'CUDA 11.1') needed to replicate the experiment. It refers to models and datasets but not the specific software environment for running them. |
| Experiment Setup | No | The paper mentions that 'λ is a hyper-parameter for balancing the regularization with the main loss function' and details the loss function. However, it does not provide specific numerical values for hyperparameters such as λ, learning rates, batch sizes, or optimizer settings in the main text. It defers some details to an appendix, which is not available. |