mlsanityData to 7 Oct 2026

Fair Representation Learning for Heterogeneous Information Networks

arXiv 2104.08769 ↗First posted 18 Apr 2021cs.AIcited by 28

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Abstract (arXiv)

Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness. A growing body of research has identified unfair AI systems and proposed methods to debias them, yet many challenges remain. Representation learning for Heterogeneous Information Networks (HINs), a fundamental building block used in complex network mining, has socially consequential applications such as automated career counseling, but there have been few attempts to ensure that it will not encode or amplify harmful biases, e.g. sexism in the job market. To address this gap, in this paper we propose a comprehensive set of de-biasing methods for fair HINs representation learning, including sampling-based, projection-based, and graph neural networks (GNNs)-based techniques. We systematically study the behavior of these algorithms, especially their capability in balancing the trade-off between fairness and prediction accuracy. We evaluate the performance of the proposed methods in an automated career counseling application where we mitigate gender bias in career recommendation. Based on the evaluation results on two datasets, we identify the most effective fair HINs representation learning techniques under different conditions.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 23 of its 44 references that are in the map. First posted on arXiv 18 Apr 2021.

Builds on

23 of 44 references.

References · 23

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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11 more
  1. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  2. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  3. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
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  5. 2017Controllable Invariance through Adversarial Feature Learningcited by 127
  6. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
  7. 2019Attenuating Bias in Word Vectorscited by 64
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 9; 2024: 6; 2025: 4; 2026: 1.

28 papers.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2022Fairness in Graph Mining: A Surveycited by 76
  3. 2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
  4. 2023Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?cited by 41
  5. 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
  6. 2022Fair Node Representation Learning via Adaptive Data Augmentationcited by 27
  7. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  8. 2022Learning fair representation with a parametric integral probability metriccited by 20
  9. 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
  10. 2023FairGAT: Fairness-aware Graph Attention Networkscited by 12
  11. 2023Bias Propagation in Federated Learningcited by 12
  12. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
16 more
  1. 2024Graph Fairness Learning under Distribution Shiftscited by 10
  2. 2025Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metricscited by 7
  3. 2022Mitigating Relational Bias on Knowledge Graphscited by 6
  4. 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluationcited by 6
  5. 2022FairNorm: Fair and Fast Graph Neural Network Trainingcited by 4
  6. 2023Adversarial Attacks on Fairness of Graph Neural Networkscited by 4
  7. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  8. 2023Equipping Federated Graph Neural Networks with Structure-aware Group Fairnesscited by 2
  9. 2023Fairness-aware Optimal Graph Filter Designcited by 2
  10. 2024MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakagecited by 2
  11. 2024A Benchmark for Fairness-Aware Graph Learningcited by 2
  12. 2025ReLU integral probability metric and its applicationscited by 1
  13. 2025Fairness-aware PageRank via Edge Reweightingcited by 1
  14. 2023A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systemscited by 0
  15. 2025SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encodingcited by 0
  16. 2026Fair Model-based Clusteringcited by 0