GREAD: Graph Neural Reaction-Diffusion Networks
Authors: Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments with 9 datasets and 28 baselines, our method, called GREAD, outperforms them in a majority of cases. |
| Researcher Affiliation | Academia | 1Yonsei University, Seoul, South Korea. |
| Pseudocode | Yes | Algorithm 1 How to train our proposed GREAD |
| Open Source Code | Yes | Our code is available at https://github. com/jeongwhanchoi/GREAD. |
| Open Datasets | Yes | Chameleon, Squirrel (Rozemberczki et al., 2021), iii) Film (Tang et al., 2009), iv, v, vi) Texas, Wisconsin and Cornell from Web KB. We also test on 3 homophilic graphs with high homophily ratios: i) Cora (Mc Callum et al., 2000), ii) Cite Seer (Sen et al., 2008), iii) Pub Med (Yang et al., 2016). |
| Dataset Splits | Yes | We report the mean and standard deviation accuracy after running each experiment with 10 fixed train/val/test splits. |
| Hardware Specification | Yes | The following software and hardware environments were used for all experiments: ...and i9 CPU, and NVIDIA RTX 3090. |
| Software Dependencies | Yes | UBUNTU 18.04 LTS, PYTHON 3.9.12, PYTORCH 1.11.0, PYTORCH GEOMETRIC 2.0.4, TORCHDIFFEQ 0.2.3, NUMPY 1.22.4, SCIPY 1.8.1, MATPLOTLIB 2.2.3, CUDA 11.3, and NVIDIA Driver 465.19 |
| Experiment Setup | Yes | We fine-tune our model within the hyperparameter search space in Table 12. Our hyperparameter search used the method of W&B Sweeps (Biewald, 2020) with a standard random search with 500 counts. We introduce the best hyperparameter configuration in Tables 13 to 16. |