Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks
Authors: Jun Yin, Chaozhuo Li, Hao Yan, Jianxun Lian, Senzhang Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that π-GNN significantly surpasses the leading interpretable GNN baselines with up to 9.98% interpretation improvement and 16.06% classification accuracy improvement. Meanwhile, π-GNN pre-trained on graph classification task also achieves the top-tier interpretation performance on node classification task, which further verifies its promising generalization performance among different downstream tasks. In this section, we conduct extensive experiments to evaluate the performance of π-GNN by answering the following two questions. |
| Researcher Affiliation | Collaboration | Jun Yin Central South University yinjun2000@csu.edu.cn Chaozhuo Li Microsoft Research Asia cli@microsoft.com Hao Yan Central South University CSUyh1999@csu.edu.cn Jianxun Lian Microsoft Research Asia jianxun.lian@microsoft.com Senzhang Wang Central South University szwang@csu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Graph-Hypergraph Transformation |
| Open Source Code | Yes | Our code and datasets are available at https://anonymous.4open.science/r/PI-GNN-F86C |
| Open Datasets | Yes | Synthetic Datasets. BA-2Motifs [10] and Spurious-Motif [13] are two widely-used synthetic datasets to evaluate the interpretation performance of the GNN explanation methods. Real-world Datasets. We use four real-world datasets, the superpixel graph dataset MNIST-75sp [40], the sentiment analysis dataset Graph-SST2 [16], and two chemical molecule datasets Mutag [18] and Ogbg-Molhiv [47]. |
| Dataset Splits | Yes | The PT-Motifs dataset is split into the training set of 50,000 graphs, the validation set of 10,000 graphs, and the testing set of 20,000 graphs. |
| Hardware Specification | Yes | All experiments are conducted on a single NVIDIA Ge Force 3090 GPU (24GB). |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, or other libraries/frameworks) that were used to run the experiments, only general mentions of methods and models. |
| Experiment Setup | Yes | During the pre-training phase, the batchsize is set as {32, 64, 128, 256} and the learning rate is set as {10 3, 5 10 3, 10 4, 10 5, 10 6}. The pre-training epoch is set as {20, 40, 60, 80}. As shown in Tables 8 and 9, we present both the downstream predictor architecture and the fine-tuning details of the graph classification datasets and the node classification datasets, respectively. |