Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated. In this context, the need to consider fairness and bias constraints while generating the representations has been well-motivated and studied to some extent in prior works. One major limitation of most of the prior works in this setting is that they do not aim to address the bias generated due to connectivity patterns in the graphs, such as varied node centrality, which leads to a disproportionate performance across nodes. In our work, we aim to address this issue of mitigating bias due to inherent graph structure in an unsupervised setting. To this end, we propose CAFIN, a centrality-aware fairness-inducing framework that leverages the structural information of graphs to tune the representations generated by existing frameworks. We deploy it on GraphSAGE (a popular framework in this domain) and showcase its efficacy on two downstream tasks - Node Classification and Link Prediction. Empirically, CAFIN consistently reduces the performance disparity across popular datasets (varying from 18 to 80% reduction in performance disparity) from various domains while incurring only a minimal cost of fairness.

翻译：图中无监督表示学习因未标注网络数据的日益丰富及所生成表示的紧凑性、丰富性与实用性而备受关注。在此背景下，生成表示时需考虑公平性与偏差约束的需求已得到充分论证，并在此前研究中有所探讨。然而，先前大多数研究存在一个主要局限：它们未致力于解决因图中连接模式（如节点中心性差异）所导致的偏差，这种偏差会造成节点间性能不均衡。本研究旨在解决无监督场景下因图固有结构引起的偏差缓解问题。为此，我们提出CAFIN——一种中心性感知的公平诱导框架，该框架利用图的结构信息调整现有框架生成的表示。我们将其部署于GraphSAGE（该领域主流框架），并在节点分类与链路预测两项下游任务中验证其有效性。实验表明，CAFIN能在仅牺牲极低公平性成本的条件下，持续降低跨领域流行数据集（性能差异缩减幅度达18%至80%）的性能差异。