Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations
<|reference_start|>Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations: In this study, we undertake a reproducibility analysis of 'Learning Fair Graph Representations Via Automated Data Augmentations' by Ling et al. (2022). We assess the validity of the original claims focused on node classification tasks and explore the performance of the Graphair framework in link prediction tasks. Our investigation reveals that we can partially reproduce one of the original three claims and fully substantiate the other two. Additionally, we broaden the application of Graphair from node classification to link prediction across various datasets. Our findings indicate that, while Graphair demonstrates a comparable fairness-accuracy trade-off to baseline models for mixed dyadic-level fairness, it has a superior trade-off for subgroup dyadic-level fairness. These findings underscore Graphair's potential for wider adoption in graph-based learning. Our code base can be found on GitHub at https://github.com/juellsprott/graphair-reproducibility.<|reference_end|>
@article{nijdam2024reproducibility,
    title={Reproducibility Study Of Learning Fair Graph Representations Via
  Automated Data Augmentations},
    author={Thijmen Nijdam, Juell Sprott, Taiki Papandreou-Lazos, Jurgen de Heus},
    journal={arXiv preprint arXiv:2409.00421},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00421},
    primaryClass={cs.LG cs.CY cs.SI}
}