提出社区级公平性评估框架，解决GNN在群体间预测偏差问题
ComFairGNN: Community Fair Graph Neural Network
- 基于社区层级设计公平性度量，揭示传统方法的评估误区
- 引入可学习核心集函数，缓解邻域分布差异导致的偏见
- 在3个基准数据集上同时提升模型准确率与公平性
图神经网络（GNN）已成为解决各类实际场景中图分析问题的主流方法。然而，由于节点属性及其邻域结构的影响，GNN可能对某些人口统计子群体产生预测偏差。现有研究多依赖简化的公平性评估指标，易造成误导。本文系统考察了当前去偏方法在不公平性评估中的有效性，提出一种社区级公平性度量策略，并在此层次上评估去偏方法。进一步，提出ComFairGNN框架，通过可学习的核心集（coreset-based）去偏函数，缓解因局部邻域分布多样性导致的偏差。在三个基准数据集上的综合实验表明，该模型在准确率和公平性指标上均表现优异。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have become the leading approach for addressing graph analytical problems in various real-world scenarios. However, GNNs may produce biased predictions against certain demographic subgroups due to node attributes and neighbors surrounding a node. Most current research on GNN fairness focuses predominantly on debiasing GNNs using oversimplified fairness evaluation metrics, which can give a misleading impression of fairness. Understanding the potential evaluation paradoxes due to the complicated nature of the graph structure is crucial for developing effective GNN debiasing mechanisms. In this paper, we examine the effectiveness of current GNN debiasing methods in terms of unfairness evaluation. Specifically, we introduce a community-level strategy to measure bias in GNNs and evaluate debiasing methods at this level. Further, We introduce ComFairGNN, a novel framework designed to mitigate community-level bias in GNNs. Our approach employs a learnable coreset-based debiasing function that addresses bias arising from diverse local neighborhood distributions during GNNs neighborhood aggregation. Comprehensive evaluations on three benchmark datasets demonstrate our model's effectiveness in both accuracy and fairness metrics.
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。