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 …

As machine learning becomes more widely used for critical applications, the need to study
its implications in privacy turns to be urgent. Given access to the target model and auxiliary …

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 …

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 …

Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured
data. However, recent works show that GNNs are vulnerable to adversarial attacks which …

Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …

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 …

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 …