mlsanityData to 14 Oct 2026

FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks

arXiv 2601.09394 ↗First posted 14 Jan 2026cs.SI cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 28 of its 53 references that are in the map. First posted on arXiv 14 Jan 2026.

Builds on

28 of 53 references.

References · 28

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16 more
  1. 2024On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systemscited by 28
  2. 2023Fair Attribute Completion on Graph with Missing Attributescited by 22
  3. 2024On the Feasibility of Simple Transformer for Dynamic Graph Modelingcited by 22
  4. 2024FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralizationcited by 20
  5. 2023Chasing Fairness in Graphs: A GNN Architecture Perspectivecited by 12
  6. 2024FUGNN: Harmonizing Fairness and Utility in Graph Neural Networkscited by 11
  7. 2024Rethinking Fair Graph Neural Networks from Re-balancingcited by 11
  8. 2024FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioningcited by 11
  9. 2024Endowing Pre-trained Graph Models with Provable Fairnesscited by 10
  10. 2024Heterogeneous Subgraph Transformer for Fake News Detectioncited by 9
  11. 2020Assessing Classifier Fairness with Collider Biascited by 8
  12. 2024Fairness Evaluation with Item Response Theorycited by 7
  13. 2024Logical Reasoning with Relation Network for Inductive Knowledge Graph Completioncited by 6
  14. 2024Masked Graph Transformer for Large-Scale Recommendationcited by 5
  15. 2025Revisiting Pre-processing Group Fairness: A Modular Benchmarking Frameworkcited by 4
  16. 2024Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classificationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Informationcited by 1
  2. 2026GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challengescited by 0
  3. 2026TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detectioncited by 0
  4. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0