FairProp：通过可微分传播层实现公平节点表示学习

中文摘要 AI 辅助

图神经网络（GNNs）是节点表示学习的标准工具，并越来越多地用于高风险场景。然而，其消息传递主干可能会放大拓扑偏差，引发公平性问题。我们研究了节点分类、链接预测和节点回归的下游预测层面的群体公平性，并为任意数量的敏感群体界定了人口统计均等差距。我们的节点分类界限在证明上不弱于最接近的先前结果。对于链接预测，我们是第一个对部署的sigmoid激活预测而非预激活代理的均等差距给出界限的工作，对于节点回归，我们提供了第一个这样的界限。在所有三个任务中，分析识别出偏差的两个不同来源：群体均值之间的分离和最终表示的组内协方差。基于这一见解，我们将公平性嵌入传播本身，通过向APPNP底层的凸平滑问题增加凸群体均值约束和组内协方差正则化器。对该问题展开投影梯度下降得到FairProp，其层将传播步骤与闭式投影配对，并证明线性收敛到唯一公平最优解。三个任务的实验表明，即使仅使用精确的群体均值均衡，FairProp也提供了强大的归纳偏置，在公平-效用权衡上优于强基线。

英文摘要

Graph neural networks (GNNs) are the standard tool for node representation learning and are increasingly used in high-stakes settings. Their message-passing backbone, however, can amplify topological bias, raising fairness concerns. We study group fairness at the level of downstream predictions for node classification, link prediction, and node regression, and bound the demographic parity gap for an arbitrary number of sensitive groups. Our node classification bound is provably no looser than the closest prior result. For link prediction, ours is the first bound on the parity gap of the deployed sigmoid-activated prediction rather than a pre-activation proxy, and for node regression we provide the first such bound. Across all three tasks, the analysis identifies two distinct sources of bias: the separation between group means and the within-group covariance of the final representations. Building on this insight, we embed fairness into propagation itself by augmenting the convex smoothing problem underlying APPNP with a convex group-mean constraint and a within-group covariance regularizer. Unfolding projected gradient descent on this problem yields FairProp, whose layers pair a propagation step with a closed-form projection and which provably converges linearly to the unique fair optimum. Experiments on three tasks show that FairProp, even with exact group-mean equalization alone, provides a strong inductive bias that achieves excellent fairness-utility trade-offs against strong baselines.

发表机构

• KU Leuven（鲁汶大学）

机构由 AI 辅助整理，请以论文原文为准。