Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable
to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool …

Graph Neural Networks (GNNs), which generalize traditional deep neural networks on
graph data, have achieved state-of-the-art performance on several graph analytical tasks …

Graph Neural Networks (GNNs) have boosted the performance for many graph-related
tasks. Despite the great success, recent studies have shown that GNNs are still vulnerable to …

Node injection attack on Graph Neural Networks (GNNs) is an emerging and practical attack
scenario that the attacker injects malicious nodes rather than modifying original nodes or …

ABSTRACT Graph Neural Networks have achieved immense success for node classification
with its power to explore the topological structure in graph data across many domains …

Abstract Graph Neural Networks (GNN) is a kind of deep learning model to process
structural and semantic features of graph data. They are widely used in node classification …

Abstract Model extraction attacks are considered to be a significant avenue of vulnerability in
machine learning. In model extraction attacks, the attacker repeatedly queries a victim model …

Graph neural networks (GNNs) have achieved state-ofthe-art performance in many graph-
based tasks such as node classification and graph classification. However, many recent …

Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining
tasks benefitting from the message passing strategy that fuses the local structure and node …

Graph neural networks (GNNs) have achieved impressive performance in various graph-
related tasks. However, recent studies have found that GNNs are vulnerable to adversarial …