# FairGT: A Fairness-aware Graph Transformer

arXiv: https://arxiv.org/abs/2404.17169 · HTML: https://mlsanity.com/paper/2404.17169/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Rethinking Graph Transformers with Spectral Attention](https://mlsanity.com/paper/2106.03893/) (2021)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Representing Long-Range Context for Graph Neural Networks with Global Attention](https://mlsanity.com/paper/2201.08821/) (2022)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs](https://mlsanity.com/paper/2206.04910/) (2022)
- [Towards a Unified Framework for Fair and Stable Graph Representation Learning](https://mlsanity.com/paper/2102.13186/) (2021)
- [Specformer: Spectral Graph Neural Networks Meet Transformers](https://mlsanity.com/paper/2303.01028/) (2023)
- [A Generalization of ViT/MLP-Mixer to Graphs](https://mlsanity.com/paper/2212.13350/) (2022)
- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [Hierarchical Graph Transformer with Adaptive Node Sampling](https://mlsanity.com/paper/2210.03930/) (2022)
- [ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond](https://mlsanity.com/paper/2303.06562/) (2023)
- [Fair Attribute Completion on Graph with Missing Attributes](https://mlsanity.com/paper/2302.12977/) (2023)
- [Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey](https://mlsanity.com/paper/2208.09590/) (2022)
- [Interpreting Unfairness in Graph Neural Networks via Training Node Attribution](https://mlsanity.com/paper/2211.14383/) (2022)
- [Hierarchical Transformer for Scalable Graph Learning](https://mlsanity.com/paper/2305.02866/) (2023)
- [Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges](https://mlsanity.com/paper/2212.05532/) (2022)
- [FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently](https://mlsanity.com/paper/2401.14702/) (2024)

## Built on by (most cited first, 29 of 29)

- [When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.05690/) (2025)
- [FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks](https://mlsanity.com/paper/2405.17034/) (2024)
- [FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning](https://mlsanity.com/paper/2412.10669/) (2024)
- [Biologically Plausible Brain Graph Transformer](https://mlsanity.com/paper/2502.08958/) (2025)
- [Brain-inspired Artificial Intelligence: A Comprehensive Review](https://mlsanity.com/paper/2408.14811/) (2024)
- [Fairness Evaluation with Item Response Theory](https://mlsanity.com/paper/2411.02414/) (2024)
- [Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning](https://mlsanity.com/paper/2603.01385/) (2026)
- [FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks](https://mlsanity.com/paper/2601.09394/) (2026)
- [LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrieval](https://mlsanity.com/paper/2508.07690/) (2025)
- [Factor Graph-based Interpretable Neural Networks](https://mlsanity.com/paper/2502.14572/) (2025)
- [Fairness in Augmented Graph Learning: A Survey](https://mlsanity.com/paper/2504.21296/) (2025)
- [Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs](https://mlsanity.com/paper/2510.18473/) (2025)
- [When to Invoke: Refining LLM Fairness with Toxicity Assessment](https://mlsanity.com/paper/2601.09250/) (2026)
- [Learning System Dynamics without Forgetting](https://mlsanity.com/paper/2407.00717/) (2024)
- [Debiasing Graph Representation Learning based on Information Bottleneck](https://mlsanity.com/paper/2409.01367/) (2024)
- [FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Prediction](https://mlsanity.com/paper/2508.07518/) (2025)
- [Bridging Semantic Understanding and Popularity Bias with LLMs](https://mlsanity.com/paper/2601.09478/) (2026)
- [NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information](https://mlsanity.com/paper/2603.28300/) (2026)
- [Causally Fair Node Classification on Non-IID Graph Data](https://mlsanity.com/paper/2505.01652/) (2025)
- [LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering](https://mlsanity.com/paper/2507.20980/) (2025)
- [Exploring the Global-to-Local Attention Scheme in Graph Transformers: An Empirical Study](https://mlsanity.com/paper/2509.14863/) (2025)
- [Estimating Fair Graphs from Graph-Stationary Data](https://mlsanity.com/paper/2510.07536/) (2025)
- [NeuroPathNet: Dynamic Path Trajectory Learning for Brain Functional Connectivity Analysis](https://mlsanity.com/paper/2510.24025/) (2025)
- [Mitigating Bias in Graph Hyperdimensional Computing](https://mlsanity.com/paper/2512.07433/) (2025)
- [FairGU: Fairness-aware Graph Unlearning in Social Networks](https://mlsanity.com/paper/2601.09469/) (2026)
- [Explaining Synergistic Effects in Social Recommendations](https://mlsanity.com/paper/2601.18151/) (2026)
- [Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics](https://mlsanity.com/paper/2607.14705/) (2026)
- [Fairness-Aware Network Embeddings: Methods, Applications, and Challenges](https://mlsanity.com/paper/2608.19381/) (2026)
- [Subgraph Filtering for Fair Graph Neural Networks](https://mlsanity.com/paper/2608.26437/) (2026)

## 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.