ELEGANT: Certified Defense on the Fairness of Graph Neural Networks

Yushun Dong, Binchi Zhang, Hanghang Tong, Jundong Li

Primary Area: societal considerations including fairness, safety, privacy

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Keywords: Algorithmic Fairness, Graph Neural Networks, Attack and Defense

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TL;DR: This paper proposes a framework named ELEGANT to achieve certification on the fairness level of GNN predictions, such that it is theoretically impossible for attackers to corrupt the fairness by perturbing node attributes and graph topology.

Abstract: 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.

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Submission Number: 3771