On Generalized Degree Fairness in Graph Neural Networks
Authors: Zemin Liu, Trung-Kien Nguyen, Yuan Fang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets demonstrate the effectiveness of our model on both accuracy and fairness metrics. |
| Researcher Affiliation | Academia | 1 National University of Singapore, Singapore 2 Singapore Management University, Singapore |
| Pseudocode | No | The paper describes its methods using equations and prose, but does not include a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We use two Wikipedia networks, Chameleon and Squirrel (Pei et al. 2020)... We also use a citation network EMNLP (Ma et al. 2021)... |
| Dataset Splits | Yes | For all the datasets, we randomly split the nodes into training, validation and test set with proportion 6:2:2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper discusses different GNN backbones but does not list specific software dependencies (e.g., Python, PyTorch, TensorFlow) with version numbers. |
| Experiment Setup | No | The paper states 'For other hyper-parameter settings, please refer to Appendix E.', indicating that specific details are not present in the main text. |