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 this https URL

本文提出了一种新的策略 PDD，它可以衡量 GNN 中表现出的偏见，并开发了一种算法来有效地估计每个训练节点对此偏见的影响力。在真实世界的数据集上进行实验验证了 PDD 的有效性和影响力估计的有效性，并演示了如何使用该框架进行 GNN 去偏见化。