Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
This work highlights a vulnerability in GNNs that could disadvantage subgroups based on sensitive attributes like race or gender, raising awareness for more robust models.
The paper tackles the problem of adversarial attacks degrading fairness in graph neural networks (GNNs) for node classification, showing that injecting adversarial links can significantly reduce fairness with low perturbation rates and minimal accuracy loss.
We present evidence for the existence and effectiveness of adversarial attacks on graph neural networks (GNNs) that aim to degrade fairness. These attacks can disadvantage a particular subgroup of nodes in GNN-based node classification, where nodes of the underlying network have sensitive attributes, such as race or gender. We conduct qualitative and experimental analyses explaining how adversarial link injection impairs the fairness of GNN predictions. For example, an attacker can compromise the fairness of GNN-based node classification by injecting adversarial links between nodes belonging to opposite subgroups and opposite class labels. Our experiments on empirical datasets demonstrate that adversarial fairness attacks can significantly degrade the fairness of GNN predictions (attacks are effective) with a low perturbation rate (attacks are efficient) and without a significant drop in accuracy (attacks are deceptive). This work demonstrates the vulnerability of GNN models to adversarial fairness attacks. We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.