Contrastive learning for fair graph representations via counterfactual graph augmentation

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2024-10-23 DOI:10.1016/j.knosys.2024.112635
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Abstract

Graph neural networks (GNNs) have exhibited excellent performance in graph representation learning. However, GNNs might inherit biases from the data, leading to discriminatory predictions. Existing study mainly concentrates on attaining fairness through counterfactuals related to node attributes, overlooking the causal impact of bias in the graph structure. Herein, we introduce a novel framework called fair contrastive learning based on counterfactual graph augmentation (FCLCA), aimed at learning counterfactual fairness by mitigating graph structure bias. FCLCA first generates two counterfactual graphs through structural augmentation. Next, we maximize the consistency between representations produced by nodes in these two counterfactual graphs using contrastive learning. In addition, FCLCA uses adversarial debiasing learning to further reduce the influence of sensitive attributes on the learned node representations. Finally, an optimized training strategy is used for contrastive learning to enhance the learning of counterfactual fairness. Comprehensive experiments conducted on four real-world datasets proved the effectiveness of FCLCA in balancing classification performance and fairness.
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通过反事实图增强对公平图表示进行对比学习
图神经网络(GNN)在图表示学习中表现出卓越的性能。然而,图神经网络可能会从数据中继承偏差,从而导致歧视性预测。现有研究主要集中在通过与节点属性相关的反事实来实现公平性,而忽略了图结构中偏差的因果影响。在这里,我们引入了一种新的框架,称为基于反事实图增强的公平对比学习(FCLCA),旨在通过减轻图结构偏差来学习反事实公平性。FCLCA 首先通过结构增强生成两个反事实图。接下来,我们利用对比学习最大化这两个反事实图中节点产生的表征之间的一致性。此外,FCLCA 还使用对抗性去杂学习来进一步降低敏感属性对所学节点表征的影响。最后,对比学习采用了优化的训练策略,以增强对反事实公平性的学习。在四个真实世界数据集上进行的综合实验证明了 FCLCA 在平衡分类性能和公平性方面的有效性。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
期刊最新文献
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