mlsanityData to 7 Oct 2026

Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks

arXiv 2408.12875 ↗First posted 23 Aug 2024cs.LG cs.SIcited by 5

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Abstract (arXiv)

Graph Neural Networks (GNNs) have become essential tools for graph representation learning in various domains, such as social media and healthcare. However, they often suffer from fairness issues due to inherent biases in node attributes and graph structure, leading to unfair predictions. To address these challenges, we propose a novel GNN framework, DAB-GNN, that Disentangles, Amplifies, and deBiases attribute, structure, and potential biases in the GNN mechanism. DAB-GNN employs a disentanglement and amplification module that isolates and amplifies each type of bias through specialized disentanglers, followed by a debiasing module that minimizes the distance between subgroup distributions. Extensive experiments on five datasets demonstrate that DAB-GNN significantly outperforms ten state-of-the-art competitors in terms of achieving an optimal balance between accuracy and fairness. The codebase of DAB-GNN is available at https://github.com/Bigdasgit/DAB-GNN

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 23 Aug 2024.

Builds on

18 of 32 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2017Improved Training of Wasserstein GANscited by 2,223
  5. 2016Variational Graph Auto-Encoderscited by 1,373
  6. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  7. 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
  8. 2022Graph Neural Networks for Link Prediction with Subgraph Sketchingcited by 81
  9. 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakagecited by 61
  10. 2023Towards Fair Graph Neural Networks via Graph Counterfactualcited by 23
  11. 2024FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralizationcited by 20
  12. 2023Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classificationcited by 19
6 more
  1. 2023Chasing Fairness in Graphs: A GNN Architecture Perspectivecited by 12
  2. 2022ChemicalX: A Deep Learning Library for Drug Pair Scoringcited by 11
  3. 2024Graph Fairness Learning under Distribution Shiftscited by 10
  4. 2023VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendationcited by 6
  5. 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluationcited by 6
  6. 2023Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classificationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphscited by 2
  2. 2025Learning Fair Graph Representations with Multi-view Information Bottleneckcited by 1
  3. 2025Estimating Fair Graphs from Graph-Stationary Datacited by 0
  4. 2025Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Informationcited by 0
  5. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0