Unsupervised representation learning on (large) graphs has received significant attention in the research community due to the compactness and richness of the learned embeddings and the abundance of unlabelled graph data. When deployed, these node representations must be generated with appropriate fairness constraints to minimize bias induced by them on downstream tasks. Consequently, group and individual fairness notions for graph learning algorithms have been investigated for specific downstream tasks. One major limitation of these fairness notions is that they do not consider the connectivity patterns in the graph leading to varied node influence (or centrality power). In this paper, we design a centrality-aware fairness framework for inductive graph representation learning algorithms. We propose CAFIN (Centrality Aware Fairness inducing IN-processing), an in-processing technique that leverages graph structure to improve GraphSAGE's representations - a popular framework in the unsupervised inductive setting. We demonstrate the efficacy of CAFIN in the inductive setting on two popular downstream tasks - Link prediction and Node Classification. Empirically, they consistently minimize the disparity in fairness between groups across datasets (varying from 18 to 80% reduction in imparity, a measure of group fairness) from different domains while incurring only a minimal performance cost.

翻译：在（大规模）图上进行无监督表示学习因所获嵌入的紧凑性与丰富性以及大量无标注图数据的存在，已受到学术界的广泛关注。当这些节点表示被部署使用时，必须施加适当的公平性约束，以最小化其对下游任务引入的偏差。为此，针对具体下游任务，研究者已探讨了图学习算法的群体公平性与个体公平性概念。这些公平性概念的一个主要局限在于，它们未考虑图中导致节点影响力（即中心性）差异的连通模式。本文设计了一种面向归纳式图表示学习算法的中心性感知公平性框架。我们提出CAFIN（中心性感知公平性引导处理技术），这是一种利用图结构来改进GraphSAGE（一种在无监督归纳式设置中广泛使用的框架）表示质量的加工中技术。我们在归纳式设置下通过链接预测与节点分类这两项常用下游任务验证了CAFIN的有效性。实验表明，该方法能在不同领域的数据集上持续缩小群体间的公平性差异（群体公平性指标“不均衡度”降低幅度从18%到80%不等），同时仅引入极小的性能代价。