  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 the bias in predictions arises is critical, as it
guides the design of GNN debiasing mechanisms. However, most existing works
overwhelmingly focus on GNN debiasing, but fall short on explaining how such
bias is induced. In this paper, we study a novel problem of interpreting GNN
unfairness through attributing it to the influence of training nodes.
Specifically, we propose a novel strategy named Probabilistic Distribution
Disparity (PDD) to measure the bias exhibited in GNNs, and develop an algorithm
to efficiently estimate the influence of each training node on such bias. We
verify the validity of PDD and the effectiveness of influence estimation
through experiments on real-world datasets. Finally, we also demonstrate how
the proposed framework could be used for debiasing GNNs. Open-source code can
be found at https://github.com/yushundong/BIND.
