Deep learning on graph structures has shown exciting results in various applications.
However, few attentions have been paid to the robustness of such models, in contrast to …

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

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 …

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 …

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, a popular class of models effective in a wide range of graph-based
learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority …

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

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

Recent studies have shown that structural perturbations are significantly effective in
degrading the accuracy of Graph Neural Networks (GNNs) in the semi-supervised node …