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..
Fair Graph Distillation
Authors: Qizhang Feng, Zhimeng (Stephen) Jiang, Ruiquan Li, Yicheng Wang, Na Zou, Jiang Bian, Xia Hu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the proposed algorithm can achieve better prediction performance-fairness trade-offs across various datasets and GNN architectures. |
| Researcher Affiliation | Academia | Qizhang Feng1, Zhimeng Jiang1, Ruiquan Li2, Yicheng Wang1, Na Zou1, Jiang Bian3, Xia Hu4 1Texas A&M University, 2University of Science and Technology of China, 3University of Florida, 3Rice University |
| Pseudocode | Yes | Algorithm 1 Fair Graph Distillation |
| Open Source Code | No | The paper does not provide an explicit statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We use five real-world datasets, including Pokec-z, Pokec-n [Dai and Wang, 2021, Takac and Zabovsky, 2012], German, Credit, and Recidivism [Agarwal et al., 2021]. |
| Dataset Splits | No | The paper does not explicitly provide specific percentages or counts for training, validation, and test dataset splits, nor does it refer to standard predefined splits for the listed datasets. |
| Hardware Specification | Yes | We encountered out-of-memory (OOM) issues when implementing GUIDE and REDRESS on an NVIDIA Ge Force RTX A5000 (24GB GPU memory) |
| Software Dependencies | No | The paper mentions software components like GNN models (GCN, SGC, Graph SAGE) and the Adam optimizer, but does not provide specific version numbers for any libraries or frameworks used. |
| Experiment Setup | Yes | Synthesizer training. We adopt Adam optimizer for synthesizer training with 0.0002 learning rate. MLPϕ consists of 3 linear layer with 128 hidden dimension. The outer loop number is 16 while the inner loop is 4 for each epoch. For each experiment, we train with a maximum of 1200 epochs and 3 independent runs. The temperature parameter γ is set to 10. X and ϕ are optimized alternatively. GNN training. We adopt Adam optimizer for GNN training with 0.005 learning rate. All GNN models are 2 layers with 256 hidden dimensions. For Pokec-z, Pokec-n, German, Credit, and Recidivism the training epochs are 1500, 1500, 4000, 1000, and 1000 respectively. |