通过图划分提升图Transformer公平性与可扩展性
FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning
- 用图划分减少高阶节点对低阶节点的不公平影响
- 在6个真实数据集上显著提升公平性，计算开销更低
- 适合关注模型公平性与大规模图分析的研究者
近期研究揭示了图Transformer（GT）模型在敏感特征定义的子群体上存在显著公平性问题。同时，GT模型计算密集、内存需求高，难以应用于大规模图。实验表明，图划分可提升GT模型的公平性并降低计算复杂度。我们通过理论分析发现，高阶节点的敏感特征会过度影响低阶节点，导致敏感特征偏差。为此，提出基于图划分的公平性感知可扩展图Transformer（FairGP），通过图划分最小化高阶节点的负面影响，并优化注意力机制，缓解全局注意力带来的偏差，从而提升公平性。在六个真实世界数据集上的广泛实证评估验证了FairGP在公平性方面优于现有先进方法。代码已开源：https://github.com/LuoRenqiang/FairGP。
原文摘要 · Abstract (English)
Recent studies have highlighted significant fairness issues in Graph Transformer (GT) models, particularly against subgroups defined by sensitive features. Additionally, GTs are computationally intensive and memory-demanding, limiting their application to large-scale graphs. Our experiments demonstrate that graph partitioning can enhance the fairness of GT models while reducing computational complexity. To understand this improvement, we conducted a theoretical investigation into the root causes of fairness issues in GT models. We found that the sensitive features of higher-order nodes disproportionately influence lower-order nodes, resulting in sensitive feature bias. We propose Fairness-aware scalable GT based on Graph Partitioning (FairGP), which partitions the graph to minimize the negative impact of higher-order nodes. By optimizing attention mechanisms, FairGP mitigates the bias introduced by global attention, thereby enhancing fairness. Extensive empirical evaluations on six real-world datasets validate the superior performance of FairGP in achieving fairness compared to state-of-the-art methods. The codes are available at https://github.com/LuoRenqiang/FairGP.
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