Graph Neural Networks (GNNs) have shown satisfying performance in various graph analytical problems. Hence, they have become the \emph{de facto} solution in a variety of decision-making scenarios. However, GNNs could yield biased results against certain demographic subgroups. Some recent works have empirically shown that the biased structure of the input network is a significant source of bias for GNNs. Nevertheless, no studies have systematically scrutinized which part of the input network structure leads to biased predictions for any given node. The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios. In this paper, we study a novel research problem of structural explanation of bias in GNNs. Specifically, we propose a novel post-hoc explanation framework to identify two edge sets that can maximally account for the exhibited bias and maximally contribute to the fairness level of the GNN prediction for any given node, respectively. Such explanations not only provide a comprehensive understanding of bias/fairness of GNN predictions but also have practical significance in building an effective yet fair GNN model. Extensive experiments on real-world datasets validate the effectiveness of the proposed framework towards delivering effective structural explanations for the bias of GNNs. Open-source code can be found at https://github.com/yushundong/REFEREE.
翻译:神经网络图(GNNs)在各种图表分析问题中表现出令人满意的表现。因此,在各种决策假设中,输入网络的结构如何影响GNN结果的偏差在很大程度上限制了GNNs在各种决策关键假设中的安全采用。然而,GNNs可能会对某些人口分组产生有偏见的结果。最近的一些工作经验显示,输入网络的偏差结构是GNNs产生偏差的一个重要来源。然而,没有任何研究系统地审查了输入网络结构的哪一部分导致对任何给定节点的偏差预测。投入网络的结构如何影响GNN结果的偏差透明度较低,这在很大程度上限制了在各种决策关键假设中安全地采用GNNs。在本文中,我们研究了关于对GNNS的偏差进行结构性解释的新研究问题。具体地说,我们提出了一个新的热后解释框架,以找出两组最能充分说明所显示的偏差之处,并最充分地促进GNNNE预测对任何给点的偏差程度。这种解释不仅全面了解GNNNN的偏差/公平性预测,而且还对建立有效的GNNNNNNN/Cs结构解释框架具有实际意义。在建立有效的、公平的GNNNNNNFS/GGNFS/G的理论模型模型上找到真正的实际的试验。