mlsanityData to 14 Oct 2026

Interpreting Unfairness in Graph Neural Networks via Training Node Attribution

arXiv 2211.14383 ↗First posted 25 Nov 2022cs.LG cs.CYcited by 18

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Abstract (arXiv)

Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially render biased predictions towards certain demographic subgroups. Understanding how the bias in predictions arises is critical, as it guides the design of GNN debiasing mechanisms. However, most existing works overwhelmingly focus on GNN debiasing, but fall short on explaining how such bias is induced. In this paper, we study a novel problem of interpreting GNN unfairness through attributing it to the influence of training nodes. Specifically, we propose a novel strategy named Probabilistic Distribution Disparity (PDD) to measure the bias exhibited in GNNs, and develop an algorithm to efficiently estimate the influence of each training node on such bias. We verify the validity of PDD and the effectiveness of influence estimation through experiments on real-world datasets. Finally, we also demonstrate how the proposed framework could be used for debiasing GNNs. Open-source code can be found at https://github.com/yushundong/BIND.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 61 of its 95 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

61 of 95 references.

References · 61

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 5; 2025: 5; 2026: 1.

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  1. 2023Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queriescited by 82
  2. 2024FairGT: A Fairness-aware Graph Transformercited by 29
  3. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  4. 2024CEB: Compositional Evaluation Benchmark for Fairness in Large Language Modelscited by 18
  5. 2023Unraveling Privacy Risks of Individual Fairness in Graph Neural Networkscited by 16
  6. 2024FUGNN: Harmonizing Fairness and Utility in Graph Neural Networkscited by 11
  7. 2023RELIANT: Fair Knowledge Distillation for Graph Neural Networkscited by 10
  8. 2025CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisitioncited by 10
  9. 2023Adversarial Attacks on Fairness of Graph Neural Networkscited by 4
  10. 2024Fairness without Demographics through Learning Graph of Gradientscited by 4
  11. 2024On Explaining Unfairness: An Overviewcited by 3
  12. 2023Fair Few-shot Learning with Auxiliary Setscited by 2
6 more
  1. 2025Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphscited by 2
  2. 2023Migrate Demographic Group For Fair GNNscited by 0
  3. 2025Estimating Fair Graphs from Graph-Stationary Datacited by 0
  4. 2025FnRGNN: Distribution-aware Fairness in Graph Neural Networkcited by 0
  5. 2025Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Informationcited by 0
  6. 2026Subgraph Filtering for Fair Graph Neural Networkscited by 0