fairGNN-WOD: Fair Graph Learning Without Complete Demographics
Authors: Zichong Wang, Fang Liu, Shimei Pan, Jun Liu, Fahad Saeed, Meikang Qiu, Wenbin Zhang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world graph datasets illustrate that fair GNN-WOD outperforms state-of-the-art baselines in achieving fairness but also maintains comparable prediction performance. |
| Researcher Affiliation | Academia | 1Florida International University, FL, USA 2University of Notre Dame, IN, USA 3University of Maryland Baltimore County, MD, USA 4Northeastern University, MA, USA 5Augusta University, GA, USA |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical equations, but it does not include a clearly labeled pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | Our experiments are conducted on three widely used datasets: the Credit dataset [Yeh and Lien, 2009], Pokecz and Pokec-n datasets [Takac and Zabovsky, 2012]. |
| Dataset Splits | No | To simulate cases of missing demographics, we mask all demographic information in the training and validation sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | where λ is the hyperparameter that balances the maximization of the ELBO and the minimization of the penalty term. ... where α and β are tunable hyperparameters controlling the weights of the various elements ... We examine the sensitivity of fair GNN-WOD by adjusting the parameters α and β across the values {1e 3, 1e 2, 1e 1, 1e0, 1e1, 1e2, 1e3}. |