mlsanityData to 14 Oct 2026

A Benchmark for Fairness-Aware Graph Learning

arXiv 2407.12112 ↗First posted 16 Jul 2024cs.LG cs.CY cs.SIcited by 2

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Abstract (arXiv)

Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware graph learning methods, which blocks practitioners from choosing appropriate ones for broader real-world applications. In this paper, we present an extensive benchmark on ten representative fairness-aware graph learning methods. Specifically, we design a systematic evaluation protocol and conduct experiments on seven real-world datasets to evaluate these methods from multiple perspectives, including group fairness, individual fairness, the balance between different fairness criteria, and computational efficiency. Our in-depth analysis reveals key insights into the strengths and limitations of existing methods. Additionally, we provide practical guidance for applying fairness-aware graph learning methods in applications. To the best of our knowledge, this work serves as an initial step towards comprehensively understanding representative fairness-aware graph learning methods to facilitate future advancements in this area.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 54 of its 88 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

54 of 88 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Promoting Fairness in Link Prediction with Graph Enhancementcited by 6
  2. 2026On the Safety of Graph Representation Learningcited by 0