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 networks (GNNs) are models that capture the dependencies between graph
data by passing messages between graph nodes and they have been widely used to …

Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs,
such as social networks. However, their vulnerability to privacy inference attacks restricts …

Graph Neural Networks (GNNs) are known to address such tasks over graph-structured
data, which is widely used to represent many real-world systems. The collection and …

Graph Neural Networks (GNNs) have increasingly become an indispensable tool in learning
from graph-structured data, catering to various applications including social network …

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 …

Recent advances in protecting node privacy on graph data and attacking graph neural
networks (GNNs) gain much attention. The eye does not bring these two essential tasks …

A graph neural network (GNN) is a type of neural network that is specifically designed to
process graph-structured data. Typically, GNNs can be implemented in two settings …

Graphs are ubiquitous data structures with nodes representing objects and edges
representing relationships between them. Graph Neural Networks (GNNs) have recently …

In this paper, we present a stealthy and effective attack that exposes privacy vulnerabilities
in Graph Neural Networks (GNNs) by inferring private links within graph-structured data …