Abstract
Graph generation models have gained increasing popularity and success across various domains. However, most research in this area has concentrated on enhancing performance, with the issue of fairness remaining largely unexplored. Existing graph generation models prioritize minimizing graph reconstruction’s expected loss, which can result in representational disparities in the generated graphs that unfairly impact marginalized groups. This paper addresses this socially sensitive issue by conducting the first comprehensive investigation of fair graph generation models by identifying the root causes of representational disparities, and proposing a novel framework that ensures consistent and equitable representation across all groups. Additionally, a suite of fairness metrics has been developed to evaluate bias in graph generation models, standardizing fair graph generation research. Through extensive experiments on five real-world datasets, the proposed framework is demonstrated to outperform existing benchmarks in terms of graph fairness while maintaining competitive prediction performance.
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Wang, Z., Wallace, C., Bifet, A., Yao, X., Zhang, W. (2023). \(\mathrm FG^2AN\): Fairness-Aware Graph Generative Adversarial Networks. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14170. Springer, Cham. https://doi.org/10.1007/978-3-031-43415-0_16
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