  Driven by the powerful representation ability of Graph Neural Networks
(GNNs), plentiful GNN models have been widely deployed in many real-world
applications. Nevertheless, due to distribution disparities between different
demographic groups, fairness in high-stake decision-making systems is receiving
increasing attention. Although lots of recent works devoted to improving the
fairness of GNNs and achieved considerable success, they all require
significant architectural changes or additional loss functions requiring more
hyper-parameter tuning. Surprisingly, we find that simple re-balancing methods
can easily match or surpass existing fair GNN methods. We claim that the
imbalance across different demographic groups is a significant source of
unfairness, resulting in imbalanced contributions from each group to the
parameters updating. However, these simple re-balancing methods have their own
shortcomings during training. In this paper, we propose FairGB, Fair Graph
Neural Network via re-Balancing, which mitigates the unfairness of GNNs by
group balancing. Technically, FairGB consists of two modules: counterfactual
node mixup and contribution alignment loss. Firstly, we select counterfactual
pairs across inter-domain and inter-class, and interpolate the ego-networks to
generate new samples. Guided by analysis, we can reveal the debiasing mechanism
of our model by the causal view and prove that our strategy can make sensitive
attributes statistically independent from target labels. Secondly, we reweigh
the contribution of each group according to gradients. By combining these two
modules, they can mutually promote each other. Experimental results on
benchmark datasets show that our method can achieve state-of-the-art results
concerning both utility and fairness metrics. Code is available at
https://github.com/ZhixunLEE/FairGB.
