Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications. Among these, recent frameworks grounded in diffusion processes provide a unifying perspective that extends traditional graph neural network formulations while addressing limitations of standard message-passing mechanisms. Despite these advances, concerns remain regarding the fairness of such models, as they may propagate or amplify biases present in the data. In this work, we introduce a fairness-aware adaptation of graph-based diffusion by modifying the underlying Laplacian operator. Our approach incorporates multiple complementary transformations, including subspace projections, spectral adjustments, and frequency-based filtering, to mitigate bias-related components. Leveraging the intrinsic smoothing properties of graph diffusion, we provide a principled analysis of the resulting behavior and establish theoretical insights into fairness properties. We evaluate the proposed framework on both synthetic and real-world datasets, demonstrating that it achieves competitive performance while improving fairness metrics with limited additional computational cost.

翻译：基于图的学习方法因其在各类应用中的强大性能而日益受到关注。其中，近期基于扩散过程的框架提供了统一视角，不仅扩展了传统图神经网络的公式体系，还解决了标准消息传递机制的局限。然而，这些模型可能传播或放大数据中存在的偏差，其公平性问题仍令人担忧。本研究通过修改底层拉普拉斯算子，提出了一种具有公平性感知的图扩散自适应方法。我们融合多种互补变换技术，包括子空间投影、频谱调整以及基于频率的滤波，以缓解与偏差相关的成分。利用图扩散的内在平滑特性，我们对所产生的行为进行了原理性分析，并建立了关于公平性属性的理论洞见。我们在合成数据集和真实世界数据集上评估了所提出的框架，结果表明该框架在提升公平性指标的同时，能以有限的额外计算成本保持具有竞争力的性能。