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..
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 | Venue PDF | 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. |