Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Understand

Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification.

• However, only few work has addressed the adversarial robustness of GNNs.
• In this paper, we first present a novel gradient-based attack method that facilitates the difficulty of tackling discrete graph data.
• When comparing to current adversarial attacks on GNNs, the results show that by only perturbing a small number of edge perturbations, including addition and deletion, our optimization-based attack can lead to a noticeable decrease in classification performance.