不补全敏感信息，用图谱理论实现社交网络中的公平编码
FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks
- 用主特征向量捕捉结构信息，零填充缺失属性保持独立性
- 在7个真实数据集上，公平性指标提升至少16%
- 适合关注隐私保护与算法公平性的图学习研究者
图变换器（GTs）在社交网络分析中应用日益广泛，但其部署常受公平性问题制约。这一问题在不完整社交网络中尤为突出，因敏感属性常因隐私和伦理限制而缺失。现有方法通常通过生成缺失属性来解决，可能引入新偏差并进一步损害用户隐私。为此，本文提出公平感知的图编码框架FairGE，无需生成敏感属性，而是直接通过谱图理论编码公平性。利用主特征向量表示结构信息，并将缺失敏感属性以零填充，以保持其独立性。理论分析表明，该方法抑制了非主谱成分的影响，从而提升公平性。在七个真实社交网络数据集上的实验验证，FairGE在统计均等性和机会均等性上较先进基线至少提升16%。源代码已开源：https://github.com/LuoRenqiang/FairGE。
原文摘要 · Abstract (English)
Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in incomplete social networks, where sensitive attributes are frequently missing due to privacy and ethical restrictions. Existing solutions commonly generate these incomplete attributes, which may introduce additional biases and further compromise user privacy. To address this challenge, FairGE (Fair Graph Encoding) is introduced as a fairness-aware framework for GTs in incomplete social networks. Instead of generating sensitive attributes, FairGE encodes fairness directly through spectral graph theory. By leveraging the principal eigenvector to represent structural information and padding incomplete sensitive attributes with zeros to maintain independence, FairGE ensures fairness without data reconstruction. Theoretical analysis demonstrates that the method suppresses the influence of non-principal spectral components, thereby enhancing fairness. Extensive experiments on seven real-world social network datasets confirm that FairGE achieves at least a 16% improvement in both statistical parity and equality of opportunity compared with state-of-the-art baselines. The source code is shown in https://github.com/LuoRenqiang/FairGE.
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