Towards Multi-Grained Explainability for Graph Neural Networks
Authors: Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 xiangwang@u.nus.edu, wuyxin@mail.ustc.edu.cn, an_zhang@nus.edu.sg xiangnanhe@gmail.com, dcscts@nus.edu.sg |
| 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. |