• Corpus ID: 245853773

FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

@article{Loveland2022FairEditPF,
  title={FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing},
  author={Donald Loveland and Jiayi Pan and Aaresh Farrokh Bhathena and Yiyang Lu},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.03681}
}
Graph Neural Networks (GNNs) have proven to excel in predictive modeling tasks where the underlying data is a graph. However, as GNNs are extensively used in human-centered applications, the issue of fairness has arisen. While edge deletion is a common method used to promote fairness in GNNs, it fails to consider when data is inherently missing fair connections. In this work we consider the unexplored method of edge addition, accompanied by deletion, to promote fairness. We propose two model… 

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