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..
Self-supervised Adversarial Purification for Graph Neural Networks
Authors: Woohyun Lee, Hogun Park
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments across diverse datasets and attack scenarios demonstrate the state-of-the-art robustness of GPR-GAE, showcasing it as an independent plug-and-play purifier for GNN classifiers. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea. |
| Pseudocode | Yes | E. Algorithms Algorithm 1 Training of GPR-GAE Algorithm 2 Multi-Step Purification with GPR-GAE |
| Open Source Code | Yes | Our code can be found in https://github.com/woodavid31/GPR-GAE. |
| Open Datasets | Yes | We conducted experiments on various datasets including Cora, Cora ML, Citeseer (Bojchevski & G unnemann 2018), Pubmed (Sen et al. 2008), OGB-ar Xiv (Hu et al. 2020), and Chameleon with removed duplicates (Platonov et al. 2023). |
| Dataset Splits | Yes | We use an inductive split with 20 labeled nodes per class for train and validation, a stratified test set of 10% of nodes, and the remaining nodes as unlabeled training data. For Chameleon and OGB-ar Xiv, we use their provided splits with fully labeled training sets. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA A100 (80GB) GPU. However, it is worth noting that GPR-GAE can be trained and applied to datasets, including OGB-ar Xiv, using an NVIDIA RTX A5000 (24GB). |
| Software Dependencies | No | The paper describes the models used and their configurations (e.g., Two-layer GCN, MLP with 64 hidden units), but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | GPR-GAE is trained using the ADAM optimizer with a learning rate of 0.01 and a weight decay of 0.0001. Training is conducted for 2000 epochs. ... When training the classifiers, a maximum of 3000 epochs is used for training, using the Adam optimizer with a learning rate of 0.01, weight decay of 0.001, and tanh Margin loss. An early stop method is used with a patience of 200 epochs. |