Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially render biased predictions towards certain demographic subgroups. Understanding how bias arises is critical, it guides design of GNN debiasing mechanisms. However, most existing works overwhelmingly focus on debiasing, bu...