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.

翻译：图Transformer（GT）在社交网络分析中的应用日益广泛，但其部署常受公平性问题的制约。这一问题在不完全社交网络中尤为关键，其中敏感属性常因隐私和伦理限制而缺失。现有解决方案通常生成这些不完整的属性，这可能引入额外偏差并进一步损害用户隐私。为应对这一挑战，本文提出了FairGE（公平图编码），作为不完全社交网络中GT的公平感知框架。FairGE不生成敏感属性，而是直接通过谱图理论编码公平性。该方法利用主特征向量表示结构信息，并通过零值填充不完整的敏感属性以保持独立性，从而无需数据重构即可确保公平性。理论分析表明，该方法抑制了非主谱分量的影响，从而提升了公平性。在七个真实社交网络数据集上的大量实验证实，与最先进的基线方法相比，FairGE在统计均等和机会均等方面至少实现了16%的性能提升。源代码发布于https://github.com/LuoRenqiang/FairGE。