mlsanityData to 14 Oct 2026

BeMap: Balanced Message Passing for Fair Graph Neural Network

arXiv 2306.04107 ↗First posted 7 Jun 2023cs.LG cs.AI cs.SIcited by 11

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Abstract (arXiv)

Fairness in graph neural networks has been actively studied recently. However, existing works often do not explicitly consider the role of message passing in introducing or amplifying the bias. In this paper, we first investigate the problem of bias amplification in message passing. We empirically and theoretically demonstrate that message passing could amplify the bias when the 1-hop neighbors from different demographic groups are unbalanced. Guided by such analyses, we propose BeMap, a fair message passing method, that leverages a balance-aware sampling strategy to balance the number of the 1-hop neighbors of each node among different demographic groups. Extensive experiments on node classification demonstrate the efficacy of BeMap in mitigating bias while maintaining classification accuracy. The code is available at https://github.com/xiaolin-cs/BeMap.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 47 of its 69 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

47 of 69 references.

References · 47

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35 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 3; 2026: 2.

11 papers.

  1. 2024How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmarkcited by 43
  2. 2023Large Language Models as Topological Structure Enhancers for Text-Attributed Graphscited by 27
  3. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  4. 2024BACKTIME: Backdoor Attacks on Multivariate Time Series Forecastingcited by 18
  5. 2025Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesicscited by 17
  6. 2024PageRank Bandits for Link Predictioncited by 14
  7. 2024GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networkscited by 2
  8. 2025Estimating Fair Graphs from Graph-Stationary Datacited by 0
  9. 2025FnRGNN: Distribution-aware Fairness in Graph Neural Networkcited by 0
  10. 2026Quantile-Free Uncertainty Quantification in Graph Neural Networkscited by 0
  11. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0