Adversarial Attacks and Defenses on Graphs Page 1 Adversarial Attacks and Defenses on
Graphs: A Review, A Tool and Empirical Studies Wei Jin†, Yaxin Li†, Han Xu†, Yiqi Wang† …

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

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 …

Deep learning models for graphs have achieved strong performance for the task of node
classification. Despite their proliferation, currently there is no study of their robustness to …

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 deep learning models, such as graph convolutional networks (GCN) achieve
remarkable performance for tasks on graph data. Similar to other types of deep models …

Recent studies have shown that graph neural networks (GNNs) are vulnerable against
perturbations due to lack of robustness and can therefore be easily fooled. Currently, most …

Recent studies have demonstrated that machine learning approaches like deep learning
methods are easily fooled by adversarial attacks. Recently, a highly-influential study …

Deep neural networks, while generalize well, are known to be sensitive to small adversarial
perturbations. This phenomenon poses severe security threat and calls for in-depth …

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 …