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

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 …

While deep neural networks have achieved great success in graph analysis, recent work
has shown that they are vulnerable to adversarial attacks. Compared with 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 neural networks (GNNs) have recently gained much attention for node and graph
classification tasks on graph-structured data. However, multiple recent works showed that an …

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean
network data. Recently, there emerge a number of works to investigate the robustness of …

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 …