论文标题
通过训练节点归因来解释图神经网络中的不公平性
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
论文作者
论文摘要
图形神经网络（GNN）已成为解决各种现实世界应用中图形分析问题的领先范式。然而，GNNS可能会对某些人口亚组产生偏见的预测。了解预测中的偏见是至关重要的，因为它指导了GNN伪造机制的设计。但是，大多数现有的作品绝大多数都集中在GNN的依据上，但缺乏解释如何诱发这种偏见的。在本文中，我们研究了通过将GNN不公平归因于训练节点的影响来解释GNN不公平的新问题。具体而言，我们提出了一种名为概率分布差异（PDD）的新型策略，以测量GNN中表现出的偏差，并开发出一种算法，以有效估计每个训练节点对这种偏见的影响。我们通过对现实世界数据集的实验来验证PDD的有效性以及影响估计的有效性。最后，我们还展示了如何将提出的框架用于辩护GNN。可以在https://github.com/yushundong/bind上找到开源代码。
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.
