GRAPHGINI: 促进图神经网络中个体和群体公平性
GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
AI总结：
GraphGini通过引入基尼系数提升图神经网络的个体和群体公平性，实验表明其在保持效用的同时显著改进了公平性。
摘要：
图神经网络（GNNs）在各种任务中展现了出色的性能，导致其在高风险决策系统中的广泛应用。然而，当缺乏公平性约束时，GNNs可能会对弱势群体或个体产生不公平的决策。本工作通过引入GraphGini，一种新的方法，将基尼系数纳入GNN框架中，以增强个体和群体公平性。我们严格证明基尼系数在GNN结果中提供了更强的鲁棒性和促进平等机会的优势，而传统的Lipschitz常数方法无法提供这些优势。此外，我们采用纳什社会福利计划，确保我们的解决方案能够产生帕累托最优的群体公平分配。在真实世界数据集上的广泛实验表明，GraphGini在显著提高个体公平性方面优于最先进的方法，同时保持了效用和群体公平性。
英文摘要：
Graph Neural Networks (GNNs) have demonstrated impressive performance across various tasks, leading to their increased adoption in high-stakes decision-making systems. However, concerns have arisen about GNNs potentially generating unfair decisions for underprivileged groups or individuals when lacking fairness constraints. This work addresses this issue by introducing GraphGini, a novel approach that incorporates the Gini coefficient to enhance both individual and group fairness within the GNN framework. We rigorously establish that the Gini coefficient offers greater robustness and promotes equal opportunity among GNN outcomes, advantages not afforded by the prevailing Lipschitz constant methodology. Additionally, we employ the Nash social welfare program to ensure our solution yields a Pareto optimal distribution of group fairness. Extensive experimentation on real-world datasets demonstrates GraphGini's efficacy in significantly improving individual fairness compared to state-of-the-art methods while maintaining utility and group fairness.
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