Despite the success of graph neural networks (GNNs), their vulnerability to adversarial
attacks poses tremendous challenges for practical applications. Existing defense methods …

Abstract Graph Neural Networks (GNNs) have demonstrated remarkable success across
diverse fields, yet remain susceptible to subtle adversarial perturbations that significantly …

Graph convolutional networks (GCNs) have been shown to be vulnerable to small
adversarial perturbations, which becomes a severe threat and largely limits their …

Graph neural networks (GNNs) have achieved significant success in numerous graph-based
applications. Unfortunately, they are sensitive to adversarial examples generated by …

Adversarial attacks on graphs have posed a major threat to the robustness of graph machine
learning (GML) models. Naturally, there is an ever-escalating arms race between attackers …

Current defenses against graph attacks often rely on certain properties to eliminate structural
perturbations by identifying adversarial edges from normal edges. However, this …

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs.
However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations …

Abstract Graph Neural Networks (GNNs) have achieved remarkable performance on diverse
graph representation learning tasks. However, recent studies have unveiled their …

With recent advancements, graph neural networks (GNNs) have shown considerable
potential for various graph-related tasks, and their applications have gained considerable …

Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …