mlsanityData to 7 Oct 2026

Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation

arXiv 2402.15988 ↗First posted 25 Feb 2024cs.SI cs.LGcited by 6

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Abstract (arXiv)

The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive subgroups. However, the current literature does not comprehensively discuss this problem, nor does it provide realistic datasets that encompass actual graph structures, anomaly labels, and sensitive attributes. To bridge this gap, we introduce a formal definition of the FairGAD problem and present two novel datasets constructed from the social media platforms Reddit and Twitter. These datasets comprise 1.2 million and 400,000 edges associated with 9,000 and 47,000 nodes, respectively, and leverage political leanings as sensitive attributes and misinformation spreaders as anomaly labels. We demonstrate that our FairGAD datasets significantly differ from the synthetic datasets used by the research community. Using our datasets, we investigate the performance-fairness trade-off in nine existing GAD and non-graph AD methods on five state-of-the-art fairness methods. Our code and datasets are available at https://github.com/nigelnnk/FairGAD

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 40 of its 89 references that are in the map. First posted on arXiv 25 Feb 2024.

Builds on

40 of 89 references.

References · 40

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28 more
  1. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  2. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  3. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
  2. 2024Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networkscited by 5
  3. 2025Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphscited by 2
  4. 2025Fairness-aware Anomaly Detection via Fair Projectioncited by 1
  5. 2026GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challengescited by 0
  6. 2026TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learningcited by 0