Fair Graph Distillation
Authors: Qizhang Feng, Zhimeng (Stephen) Jiang, Ruiquan Li, Yicheng Wang, Na Zou, Jiang Bian, Xia Hu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |