mlsanityData to 7 Oct 2026

FairGT: A Fairness-aware Graph Transformer

arXiv 2404.17169 ↗First posted 26 Apr 2024cs.LG cs.CYcited by 29

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Abstract (arXiv)

The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph information without relying on message-passing mechanisms, conventional fairness-aware graph learning methods cannot be directly applicable to address these issues. To tackle this challenge, we propose FairGT, a Fairness-aware Graph Transformer explicitly crafted to mitigate fairness concerns inherent in GTs. FairGT incorporates a meticulous structural feature selection strategy and a multi-hop node feature integration method, ensuring independence of sensitive features and bolstering fairness considerations. These fairness-aware graph information encodings seamlessly integrate into the Transformer framework for downstream tasks. We also prove that the proposed fair structural topology encoding with adjacency matrix eigenvector selection and multi-hop integration are theoretically effective. Empirical evaluations conducted across five real-world datasets demonstrate FairGT's superiority in fairness metrics over existing graph transformers, graph neural networks, and state-of-the-art fairness-aware graph learning approaches.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 24 of its 36 references that are in the map. First posted on arXiv 26 Apr 2024.

Builds on

24 of 36 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  5. 2020Simple and Deep Graph Convolutional Networkscited by 857
  6. 2020A Generalization of Transformer Networks to Graphscited by 653
  7. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  8. 2020Fairness in Machine Learning: A Surveycited by 369
  9. 2022Representing Long-Range Context for Graph Neural Networks with Global Attentioncited by 193
  10. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  11. 2021Graph Learning: A Surveycited by 140
  12. 2022NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphscited by 137
12 more
  1. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  2. 2023Specformer: Spectral Graph Neural Networks Meet Transformerscited by 102
  3. 2022A Generalization of ViT/MLP-Mixer to Graphscited by 93
  4. 2022Fairness in Graph Mining: A Surveycited by 76
  5. 2022Hierarchical Graph Transformer with Adaptive Node Samplingcited by 72
  6. 2023ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyondcited by 39
  7. 2023Fair Attribute Completion on Graph with Missing Attributescited by 22
  8. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19
  9. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18
  10. 2023Hierarchical Transformer for Scalable Graph Learningcited by 17
  11. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  12. 2024FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficientlycited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 13; 2026: 10.

29 papers.

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  1. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generationcited by 46
  2. 2024FUGNN: Harmonizing Fairness and Utility in Graph Neural Networkscited by 11
  3. 2024FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioningcited by 11
  4. 2025Biologically Plausible Brain Graph Transformercited by 9
  5. 2024Brain-inspired Artificial Intelligence: A Comprehensive Reviewcited by 8
  6. 2024Fairness Evaluation with Item Response Theorycited by 7
  7. 2026Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuningcited by 7
  8. 2026FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networkscited by 4
  9. 2025LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrievalcited by 3
  10. 2025Factor Graph-based Interpretable Neural Networkscited by 2
  11. 2025Fairness in Augmented Graph Learning: A Surveycited by 2
  12. 2025Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphscited by 2
17 more
  1. 2026When to Invoke: Refining LLM Fairness with Toxicity Assessmentcited by 2
  2. 2024Learning System Dynamics without Forgettingcited by 1
  3. 2024Debiasing Graph Representation Learning based on Information Bottleneckcited by 1
  4. 2025FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Predictioncited by 1
  5. 2026Bridging Semantic Understanding and Popularity Bias with LLMscited by 1
  6. 2026NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Informationcited by 1
  7. 2025Causally Fair Node Classification on Non-IID Graph Datacited by 0
  8. 2025LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clusteringcited by 0
  9. 2025Exploring the Global-to-Local Attention Scheme in Graph Transformers: An Empirical Studycited by 0
  10. 2025Estimating Fair Graphs from Graph-Stationary Datacited by 0
  11. 2025NeuroPathNet: Dynamic Path Trajectory Learning for Brain Functional Connectivity Analysiscited by 0
  12. 2025Mitigating Bias in Graph Hyperdimensional Computingcited by 0
  13. 2026FairGU: Fairness-aware Graph Unlearning in Social Networkscited by 0
  14. 2026Explaining Synergistic Effects in Social Recommendationscited by 0
  15. 2026Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographicscited by 0
  16. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0
  17. 2026Subgraph Filtering for Fair Graph Neural Networkscited by 0