通过融合拓扑与特征相似性，提升图神经网络的个体公平性。
SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding
- 基于拓扑与特征双视角构建相似性度量，识别潜在不公平根源。
- 在多个真实数据集上，显著优于现有公平性方法，且不损失模型性能。
- 适合关注图学习公平性的研究人员与应用开发者。
图神经网络中的个体公平性（IF）要求相似个体获得相似预测结果，但当前研究对不公平成因缺乏清晰理解，且对相似性判定考虑不足。本文分析发现IF与相似性一致性密切相关，即基于图结构和节点特征识别的相似性存在偏差。为此，提出拓扑融合与特征融合两种相似性度量方法，并设计相似性感知的公平图神经网络SaGIF。其核心思想是独立学习个体相似性表示并加以整合，从而提升整体公平性。实验在多个真实数据集上验证了所提方法的有效性：SaGIF在保持模型性能的同时，持续优于现有先进公平性方法。代码已开源。
原文摘要 · Abstract (English)
Individual fairness (IF) in graph neural networks (GNNs), which emphasizes the need for similar individuals should receive similar outcomes from GNNs, has been a critical issue. Despite its importance, research in this area has been largely unexplored in terms of (1) a clear understanding of what induces individual unfairness in GNNs and (2) a comprehensive consideration of identifying similar individuals. To bridge these gaps, we conduct a preliminary analysis to explore the underlying reason for individual unfairness and observe correlations between IF and similarity consistency, a concept introduced to evaluate the discrepancy in identifying similar individuals based on graph structure versus node features. Inspired by our observations, we introduce two metrics to assess individual similarity from two distinct perspectives: topology fusion and feature fusion. Building upon these metrics, we propose Similarity-aware GNNs for Individual Fairness, named SaGIF. The key insight behind SaGIF is the integration of individual similarities by independently learning similarity representations, leading to an improvement of IF in GNNs. Our experiments on several real-world datasets validate the effectiveness of our proposed metrics and SaGIF. Specifically, SaGIF consistently outperforms state-of-the-art IF methods while maintaining utility performance. Code is available at: https://github.com/ZzoomD/SaGIF.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。