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..
Reinforcement Learning Enhanced Explainer for Graph Neural Networks
Authors: Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, Dongsheng Li
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real datasets show that RG-Explainer outperforms state-of-the-art GNN explainers. |
| Researcher Affiliation | Collaboration | 1Microsoft Research Asia EMAIL 2The Hong Kong University of Science and Technology EMAIL 3Fudan University EMAIL 4East China Normal University EMAIL |
| Pseudocode | Yes | Due to the space limitation, we move the pseudocode, implementation details and ablation study to the supplementary materials. |
| Open Source Code | Yes | We also attach our codes in the supplementary materials. |
| Open Datasets | Yes | We use six datasets, in which four synthetic datasets (BA-shapes, BA-Community, Tree Cycles and Tree-Grid) are used for the node classification task and two datasets (BA-2motifs and Mutagenicity) are used for the graph classificition task. |
| Dataset Splits | No | Specifically, we vary the training set sizes from {10%, 30%, 50%, 70%, 90%} and take the remaining instances for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | For fairness, we follow the experimental setup in [17, 12], i.e., the same datasets, trained GNN model and evaluation metrics. Besides, we also utilize the same fine-tuned parameters in [12] for our competitors, GNNExplainer and PGExplainer. |