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 …

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 …

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 neural networks, which learn aggregations over neighbor nodes, have
achieved great performance in node classification tasks. However, recent studies reported …

Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to
adversarial attacks on the graph structure. Although multiple works have been proposed to …

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

In this paper, we study the robustness of graph convolutional networks (GCNs). Despite the
good performance of GCNs on graph semi-supervised learning tasks, previous works have …

Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks
such as node classification and graph classification. Recent researches show that graph …

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

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 …