Despite its success in the image domain, adversarial training did not (yet) stand out as an
effective defense for Graph Neural Networks (GNNs) against graph structure perturbations …

Deep learning methods for graphs achieve remarkable performance on many tasks.
However, despite the proliferation of such methods and their success, recent findings …

A cursory reading of the literature suggests that we have made a lot of progress in designing
effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard …

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

Despite the recent success of graph convolution networks (GCNs) in modeling graph
structured data, its vulnerability to adversarial attacks have been revealed and attacks on …

Graph neural networks (GNNs) have achieved great success on various graph tasks.
However, recent studies have re-vealed that GNNs are vulnerable to adversarial attacks …

Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but
adversarially designed perturbations, known as adversarial examples. To address this …

Graph Neural Networks (GNNs) have demonstrated their powerful capability in learning
representations for graph-structured data. Consequently, they have enhanced the …

Deep learning models for graphs have achieved strong performance for the task of node
classification. Despite their proliferation, little is known about their robustness to adversarial …

Graph Convolutional Networks (GCNs) are an emerging type of neural network model on
graphs which have achieved state-of-the-art performance in the task of node classification …