Fairness-Aware Graph Learning: A Benchmark

Yushun Dong, Song Wang, Zhenyu Lei, Zaiyi Zheng, Jing Ma, Chen Chen, Jundong Li

Keywords: Graph Learning Algorithms, Algorithmic Fairness, Performance Benchmark

TL;DR: This paper presents a comprehensive benchmark for the representative fairness-aware graph learning algorithms, which provides in-depth analysis and practical guidance to facilitate their broader applications and future advancements.

Abstract: 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.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 7880