  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.
