Machine learning over graphs has recently attracted growing attention due to its ability to analyze and learn complex relations within critical interconnected systems. However, the disparate impact that is amplified by the use of biased graph structures in these algorithms has raised significant concerns for the deployment of them in real-world decision systems. In addition, while synthetic graph generation has become pivotal for privacy and scalability considerations, the impact of generative learning algorithms on the structural bias has not yet been investigated. Motivated by this, this work focuses on the analysis and mitigation of structural bias for both real and synthetic graphs. Specifically, we first theoretically analyze the sources of structural bias that result in disparity for the predictions of dyadic relations. To alleviate the identified bias factors, we design a novel fairness regularizer that offers a versatile use. Faced with the bias amplification in graph generation models that is brought to light in this work, we further propose a fair graph generation framework, FairWire, by leveraging our fair regularizer design in a generative model. Experimental results on real-world networks validate that the proposed tools herein deliver effective structural bias mitigation for both real and synthetic graphs.

翻译：近年来，基于图的机器学习因其能够分析和学习关键互联系统中的复杂关系而受到日益增长的关注。然而，这些算法中使用的有偏图结构所放大的差异性影响，引发了对其在现实决策系统中部署的严重担忧。此外，尽管合成图生成已成为隐私和可扩展性考虑的关键，但生成式学习算法对结构性偏差的影响尚未被研究。受此启发，本文聚焦于对真实图和合成图中结构性偏差的分析与缓解。具体而言，我们首先从理论上分析了导致二元关系预测差异的结构性偏差来源。为缓解已识别的偏差因素，我们设计了一种新颖的公平性正则化器，具有广泛适用性。针对本工作中揭示的图生成模型中的偏差放大问题，我们进一步提出了一种公平图生成框架FairWire，通过将公平正则化器设计融入生成模型。在真实世界网络上的实验结果验证了本文提出的工具在缓解真实图和合成图的结构性偏差方面具有有效性。