Adversarial Attacks on Fairness of Graph Neural Networks

arXiv:2310.13822v2 Announce Type: replace
Abstract: Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e.g., female) in graph-based applications. Although these methods greatly improve the algorithmic fairness of GNNs, the fairness can be easily corrupted by carefully designed adversarial attacks. In this paper, we investigate the problem of adversarial attacks on fairness of GNNs and propose G-FairAttack, a general framework for attacking various types of fairness-aware …

abstract adversarial adversarial attacks algorithmic fairness applications arxiv attacks attention bias cs.lg fairness gnns graph graph-based graph neural networks networks neural networks predictions reduce type