Graph topologies have been detected in data captured in social networks, biological
networks, traffic networks, smart grids, and ecological networks. Graphs provide effective …

While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph
representation learning in many applications, the neighborhood aggregation scheme …

Knowledge graphs contain a large amount of entity and relational data, and graph neural
networks, as a class of efficient graph representation techniques based on deep learning …

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 …

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 …

Graphs provide a unique representation of real-world data. However, recent studies found
that inference attacks can extract private property information of graph data from trained …

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 mining graph-structured
data. Despite the superior performance of GNNs in learning graph representations, serious …

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 …

Latent graph structure and stimulus of graph-structured data contain critical private
information, such as brain disorders in functional magnetic resonance imaging data, and …