FairNorm: Fair and Fast Graph Neural Network Training. (arXiv:2205.09977v1 [cs.LG])

Graph neural networks (GNNs) have been demonstrated to achieve
state-of-the-art for a number of graph-based learning tasks, which leads to a
rise in their employment in various domains. However, it has been shown that
GNNs may inherit and even amplify bias within training data, which leads to
unfair results towards certain sensitive groups. Meanwhile, training of GNNs
introduces additional challenges, such as slow convergence and possible
instability. Faced with these limitations, this work proposes FairNorm, a
unified normalization framework that …

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