标题： 通过公平感知子图扩散的无偏GNN学习 显示英文标题

标题： Unbiased GNN Learning via Fairness-Aware Subgraph Diffusion

摘要： 图神经网络（GNNs）在处理跨多个领域的各种图相关任务方面表现出显著的有效性。 然而，一个重大挑战在于它们倾向于生成有偏见的预测，尤其是在敏感节点属性方面，如年龄和性别。 这些偏见存在于许多机器学习模型中，由于消息传递机制，使得节点之间相互影响，从而在GNNs中被放大，使得做出公平的预测任务变得尤为困难。 这个问题在模型公平性至关重要的关键领域中尤为重要。 在本文中，我们提出了一种新颖的生成式公平感知子图扩散（FASD）方法，用于无偏的GNN学习。 该方法首先从原始的大输入图中战略性地采样小的子图，然后通过基于随机微分方程（SDEs）的生成式公平感知图扩散过程进行子图去偏。 为了有效扩散输入数据中的不公平性，我们在前向扩散过程中向子图引入额外的对抗性偏差扰动，并训练基于得分的模型来预测这些应用的扰动，使其能够学习数据中存在偏差的潜在动态。 随后，训练好的基于得分的模型通过反向扩散过程进一步对原始子图样本进行去偏。 最后，FASD通过对去偏的子图进行标准的GNN学习，在输入图上产生公平的节点预测。 实验结果表明，所提出的方法在多个基准数据集上优于最先进的公平GNN基线方法。 显示英文摘要

摘要： Graph Neural Networks (GNNs) have demonstrated remarkable efficacy in tackling a wide array of graph-related tasks across diverse domains. However, a significant challenge lies in their propensity to generate biased predictions, particularly with respect to sensitive node attributes such as age and gender. These biases, inherent in many machine learning models, are amplified in GNNs due to the message-passing mechanism, which allows nodes to influence each other, rendering the task of making fair predictions notably challenging. This issue is particularly pertinent in critical domains where model fairness holds paramount importance. In this paper, we propose a novel generative Fairness-Aware Subgraph Diffusion (FASD) method for unbiased GNN learning. The method initiates by strategically sampling small subgraphs from the original large input graph, and then proceeds to conduct subgraph debiasing via generative fairness-aware graph diffusion processes based on stochastic differential equations (SDEs). To effectively diffuse unfairness in the input data, we introduce additional adversary bias perturbations to the subgraphs during the forward diffusion process, and train score-based models to predict these applied perturbations, enabling them to learn the underlying dynamics of the biases present in the data. Subsequently, the trained score-based models are utilized to further debias the original subgraph samples through the reverse diffusion process. Finally, FASD induces fair node predictions on the input graph by performing standard GNN learning on the debiased subgraphs. Experimental results demonstrate the superior performance of the proposed method over state-of-the-art Fair GNN baselines across multiple benchmark datasets.