Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs),
aggregate the message of nodes' neighbors and structure information to acquire expressive …

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 …

In light of the wide application of Graph Neural Networks (GNNs), Membership Inference
Attack (MIA) against GNNs raises severe privacy concerns, where training data can be …

Many real-world data comes in the form of graphs, such as social networks and protein
structure. To fully utilize the information contained in graph data, a new family of machine …

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 …

Recent research has shown that machine learning (ML) models are vulnerable to privacy
attacks that leak information about the training data. In this work, we consider Graph Neural …

Many data mining tasks rely on graphs to model relational structures among individuals
(nodes). Since relational data are often sensitive, there is an urgent need to evaluate the …

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 …

Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new
family of machine learning (ML) models, have been proposed to fully leverage graph data to …

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …