FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing. (arXiv:2201.03681v1 [cs.LG])

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-agnostic algorithms …

arxiv fairness graph graph neural networks networks neural networks