FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912831526404096 |
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| author | Luo, Renqiang Huang, Huafei Tang, Tao Ren, Jing Xu, Ziqi Hou, Mingliang Dai, Enyan Xia, Feng |
| author_facet | Luo, Renqiang Huang, Huafei Tang, Tao Ren, Jing Xu, Ziqi Hou, Mingliang Dai, Enyan Xia, Feng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09394 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks Luo, Renqiang Huang, Huafei Tang, Tao Ren, Jing Xu, Ziqi Hou, Mingliang Dai, Enyan Xia, Feng Social and Information Networks Artificial Intelligence 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. |
| title | FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2601.09394 |