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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
GREAD: Graph Neural Reaction-Diffusion Networks
Authors: Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho
ICML 2023 | Venue PDF | 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. |