Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years. Nevertheless, due to the vulnerabilities of GNNs, it has been empirically proved that malicious attackers could easily corrupt the fairness level of their predictions by adding perturbations to the input graph data. In this paper, we take crucial steps to study a novel problem of certifiable defense on the fairness level of GNNs. Specifically, we propose a principled framework named ELEGANT and present a detailed theoretical certification analysis for the fairness of GNNs. ELEGANT takes any GNNs as its backbone, and the fairness level of such a backbone is theoretically impossible to be corrupted under certain perturbation budgets for attackers. Notably, ELEGANT does not have any assumption over the GNN structure or parameters, and does not require re-training the GNNs to realize certification. Hence it can serve as a plug-and-play framework for any optimized GNNs ready to be deployed. We verify the satisfactory effectiveness of ELEGANT in practice through extensive experiments on real-world datasets across different backbones of GNNs, where ELEGANT is also demonstrated to be beneficial for GNN debiasing. Open-source code can be found at https://github.com/yushundong/ELEGANT.

翻译：图神经网络（GNNs）近年来已成为各类基于图的任务中突出的图学习模型。然而，由于GNNs的脆弱性，实验已证明恶意攻击者可通过向输入图数据添加扰动，轻易破坏其预测的公平性水平。本文迈出关键一步，研究GNNs公平性可认证防御这一新问题。具体而言，我们提出名为ELEGANT的框架化方法，并对GNNs的公平性给出详细的理论认证分析。ELEGANT以任意GNN作为骨干网络，在特定攻击者扰动预算下，该骨干网络的公平性水平在理论上不可能被破坏。值得注意的是，ELEGANT不对GNN结构或参数作任何假设，也无需重新训练GNN来实现认证，因此可作为即插即用框架，适用于任何已优化并待部署的GNN。我们通过在不同GNN骨干网络上的真实数据集进行广泛实验，验证了ELEGANT在实践中的显著有效性，并同时证明ELEGANT有益于GNN去偏。开源代码见https://github.com/yushundong/ELEGANT。