mlsanityData to 7 Oct 2026

Fair Attribute Completion on Graph with Missing Attributes

arXiv 2302.12977 ↗First posted 25 Feb 2023cs.LG cs.AIcited by 22

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Abstract (arXiv)

Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available for model training and then makes fair predictions. In practice, however, the attributes of some nodes might not be accessible due to missing data or privacy concerns, which makes fair graph learning even more challenging. In this paper, we propose FairAC, a fair attribute completion method, to complement missing information and learn fair node embeddings for graphs with missing attributes. FairAC adopts an attention mechanism to deal with the attribute missing problem and meanwhile, it mitigates two types of unfairness, i.e., feature unfairness from attributes and topological unfairness due to attribute completion. FairAC can work on various types of homogeneous graphs and generate fair embeddings for them and thus can be applied to most downstream tasks to improve their fairness performance. To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems. Experimental results on benchmark datasets show that our method achieves better fairness performance with less sacrifice in accuracy, compared with the state-of-the-art methods of fair graph learning. Code is available at: https://github.com/donglgcn/FairAC.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 28 of its 48 references that are in the map. First posted on arXiv 25 Feb 2023.

Builds on

28 of 48 references.

References · 28

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16 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 5; 2026: 5.

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  1. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  2. 2024FairGT: A Fairness-aware Graph Transformercited by 29
  3. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  4. 2024FUGNN: Harmonizing Fairness and Utility in Graph Neural Networkscited by 11
  5. 2024FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioningcited by 11
  6. 2024Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competenciescited by 9
  7. 2024No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Userscited by 9
  8. 2025Toward General and Robust LLM-enhanced Text-attributed Graph Learningcited by 6
  9. 2026FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networkscited by 4
  10. 2025Incomplete Graph Learning: A Comprehensive Surveycited by 3
  11. 2024Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networkscited by 1
  12. 2025Fairness-Aware Graph Representation Learning with Limited Demographic Informationcited by 1
10 more
  1. 2026Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solutioncited by 1
  2. 2023Migrate Demographic Group For Fair GNNscited by 0
  3. 2023Fair Graph Machine Learning under Adversarial Missingness Processescited by 0
  4. 2024Towards Real-world Debiasing: Rethinking Evaluation, Challenge, and Solutioncited by 0
  5. 2024Reproducibility study of FairACcited by 0
  6. 2025Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Thingscited by 0
  7. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  8. 2026FairGU: Fairness-aware Graph Unlearning in Social Networkscited by 0
  9. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0
  10. 2026Investigating the Effect of k-NN Preprocessing on Developing Graph Neural Networks: A Fairness-Based Perspectivecited by 0