RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
Authors: Jiaxing Zhang, Zhuomin Chen, hao mei, Longchao Da, Dongsheng Luo, Hua Wei
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed method on three benchmark datasets and a real-life dataset introduced by us, and extensive experiments demonstrate its effectiveness in interpreting GNN models in regression tasks. |
| Researcher Affiliation | Academia | 1New Jersey Institute of Technology, 2Florida International University, 3Arizona State University |
| Pseudocode | Yes | Algorithm 1 Graph Mix-up Algorithm |
| Open Source Code | Yes | Our data and code are available at: https://github.com/jz48/Reg Explainer |
| Open Datasets | Yes | We formulate Three synthetic datasets and a real-world dataset, as is shown in Table 1, in order to address the lack of graph regression datasets with ground-truth explanation. The datasets include: BA-Motif-Volume and BA-Motif-Counting, which are based on BA-shapes [23], Triangles [39], and Crippen [40]. |
| Dataset Splits | Yes | We split the dataset into 8:1:1, where we train the GNN base model with 8 folds, and train and test explainer models with 1 fold respectively. |
| Hardware Specification | Yes | All experiments are conducted on a Linux machine (Ubuntu 16.04.4 LTS (GNU/Linux 4.4.0-210generic x86_64)) with 4 NVIDIA TITAN Xp (12 GB) GPUs. |
| Software Dependencies | Yes | All codes are written with the Python version 3.8.13 with Py Torch 1.12.1 and Py Torch Geometric (Py G) 2.1.0.post1, torch-scatter 2.0.9, and torch-sparse 0.6.15. |
| Experiment Setup | Yes | Additionally, we set all variants with the same configurations as original Reg Explainer, including learning rate, training epochs, and hyperparameters η, α, and β. |