发表机构
University of the Aegean（爱琴海大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出在GCN中嵌入均等化几率公平约束，并系统分析k-NN图构建的邻域大小对分类性能与公平性的影响，实验表明公平约束有效缓解组间差异且不显著牺牲准确性。
AI 中文摘要
本文开发了一种设计公平图卷积神经网络（GCNs）的方法，并在多个应用数据集上进行了测试。用作网络输入的图通过基于k近邻的预处理过程构建，同时公平性问题依据均等化几率准则进行考量。为有效整合上述异构信息，均等化几率准则通过一个额外的公平驱动损失函数项直接嵌入模型的优化目标中。所提出的方法探究了在图构建过程中改变k-NN算法的邻域大小如何影响所得模型的分类性能和公平性。在三个具有已知偏差的真实世界表格数据集上进行了广泛实验，评估了图结构与公平性强制之间的相互作用。结果表明，参数k的取值选择对性能趋势有关键影响，根据数据集特征，性能可能稳步提升或在中间值处达到峰值，而公平性约束的应用显著缓解了由受保护变量定义的各组之间在假阳性率和假阴性率上的差异，且不会对整体准确性造成重大牺牲。本研究强调了在基于GCN的学习中联合优化图构建过程和公平性目标的重要性，为构建更公平、更有效的基于图的模型提供了一种系统方法。
英文摘要
In this paper, a methodology to design fair graph convolutional neural networks (GCNs) is developed and tested over several application data sets. The graphs that are used as inputs to the network are constructed by a k-nearest neighbor-based preprocessing procedure, while fairness issues are considered in terms of the equalized odds criterion. To effectively incorporate the above heterogenous information, the equalized odds criterion is directly embedded into the model's optimization objective through an additional fairness-driven loss functional term. The proposed methodology investigates how varying the neighborhood size in the k-NN algorithm during graph construction influences both the classification performance and the fairness of the resulting models. Extensive experimentation is conducted on three real-world tabular datasets with known biases, evaluating the interplay between graph structure and fairness enforcement. The results demonstrate that the choice of the value of the parameter k critically impacts the performance trends, either steadily improving or peaking at intermediate values depending on dataset characteristics, while the application of fairness constraints significantly mitigates disparities in false positive and false negative rates across groups defined by the protected variable at hand, without incurring major sacrifices in overall accuracy. This study highlights the importance of jointly optimizing the graph construction process and fairness objectives in GCN-based learning, providing a systematic approach toward building more equitable and effective graph-based models.
CommentsAccepted for presentation at the 2025 16th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE. Proceedings publication pending
