Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
Authors: Fang Wu, Siyuan Li, Xurui Jin, Yinghui Jiang, Dragomir Radev, Zhangming Niu, Stan Z. Li
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and real-world datasets show the effectiveness of our Match Explainer by outperforming all state-of-the-art parametric baselines with significant margins. Results also demonstrate that Match Drop is a general scheme to be equipped with GNNs for enhanced performance. |
| Researcher Affiliation | Collaboration | 1School of Engineering, Westlake University, Hangzhou, China 2Mindrank AI, Hangzhou, China 3Department of Computer Science, Yale University, New Haven, United States. |
| Pseudocode | Yes | Algorithm 1 Workflow of Match Explainer |
| Open Source Code | Yes | The code is available at https://github. com/smiles724/Match Explainer. |
| Open Datasets | Yes | Following Wang et al. (2021b), we use four standard datasets... MUTAG (Debnath et al., 1991; Kazius et al., 2005)... BA-3Motif... MNIST... VG-5 (Pope et al., 2019; Krishna et al., 2017). |
| Dataset Splits | No | The paper mentions 'full training and validation data as the reference set' and 'testing accuracy' but does not provide specific percentages or counts for training, validation, and test splits. |
| Hardware Specification | Yes | All experiments are conducted on a single A100 PCIE GPU (40GB). |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not provide specific version numbers for software dependencies such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | Regarding the re-implementation of Refine in BA-3Motif, we use the original code with the same hyperparameters, and we adopt Adam optimizer (Kingma & Ba, 2014) and set the learning rate of pre-training and fine-tuning as 1e-3 and 1e-4, respectively. |