  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 reduces the bias in GNN-based learning
while also providing provably faster convergence. Specifically, FairNorm
employs fairness-aware normalization operators over different sensitive groups
with learnable parameters to reduce the bias in GNNs. The design of FairNorm is
built upon analyses that illuminate the sources of bias in graph-based
learning. Experiments on node classification over real-world networks
demonstrate the efficiency of the proposed scheme in improving fairness in
terms of statistical parity and equal opportunity compared to fairness-aware
baselines. In addition, it is empirically shown that the proposed framework
leads to faster convergence compared to the naive baseline where no
normalization is employed.
