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..
Towards Multi-Grained Explainability for Graph Neural Networks
Authors: Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world datasets show the superiority of our explainer, in terms of AUC on explaining graph classification over the leading baselines. |
| Researcher Affiliation | Academia | Xiang Wang , Ying-Xin Wu , An Zhang , Xiangnan He , Tat-Seng Chua Sea-NEx T Joint Lab National University of Singapore University of Science and Technology of China EMAIL, EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our codes and datasets are available at https://github.com/Wuyxin/Re Fine. |
| Open Datasets | Yes | Molecule graph classification. We use the Mutagenicity dataset [40, 41]... Scene graph classification. Following the previous work [10], we select 4, 443 (images, scene graphs) pairs from Visual Genome [43]... Handwriting graph classification. We use the MNIST superpixel dataset [45]... Motif graph classification. We follow prior studies [6, 7] to create a synthetic dataset, BA-3motif... |
| Dataset Splits | No | The paper mentions 'testing accuracy' and 'testing dataset' for evaluation, but does not explicitly provide training/validation/test dataset splits or cross-validation details for their own model. |
| Hardware Specification | Yes | All experiments are done on a single Tesla V100 SXM2 GPU (32 GB). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For our Re Fine framework, we use the Adam optimizer and set the learning rate of pre-training and fine-tuning as 1e-3 and 1e-4, respectively. |