mlsanityData to 7 Oct 2026

Reproducibility study of FairAC

arXiv 2406.03314 ↗First posted 5 Jun 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

This work aims to reproduce the findings of the paper "Fair Attribute Completion on Graph with Missing Attributes" written by Guo, Chu, and Li arXiv:2302.12977 by investigating the claims made in the paper. This paper suggests that the results of the original paper are reproducible and thus, the claims hold. However, the claim that FairAC is a generic framework for many downstream tasks is very broad and could therefore only be partially tested. Moreover, we show that FairAC is generalizable to various datasets and sensitive attributes and show evidence that the improvement in group fairness of the FairAC framework does not come at the expense of individual fairness. Lastly, the codebase of FairAC has been refactored and is now easily applicable for various datasets and models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 5 Jun 2024.

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13 of 23 references.

References · 13

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  4. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  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. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  8. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  9. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  10. 2022Fairness in Graph Mining: A Surveycited by 76
  11. 2020Learning on Attribute-Missing Graphscited by 42
  12. 2023Fair Attribute Completion on Graph with Missing Attributescited by 22
1 more
  1. 2023GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hintcited by 6

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