With the ever-growing attention on communication security, machine learning-based
network intrusion detection system (NIDS) is widely utilized to meet different security …

To develop effective and efficient graph similarity learning (GSL) models, a series of data-
driven neural algorithms have been proposed in recent years. Although GSL models are …

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

Recent studies have introduced privacy-preserving graph neural networks to safeguard the
privacy of sensitive link information in graphs. However, existing link protection mechanisms …

Most social networks can be modeled as heterogeneous graphs. Recently, advanced graph
learning methods exploit the rich node properties and topological relationships for …

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node
representations for various graph inference tasks. However, learning over graph data can …

Graph neural networks (GNNs) can learn the node representations to capture both node
features and graph topology information through the message passing mechanism …

Abstract Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on
graphs, demonstrating exceptional performance in various domains. However, as GNNs …

Graph Neural Networks (GNNs) as an emerging technique have shown excellent
performance in a variety of fields, such as social networks and recommendation systems …

With the ever-growing interest in modeling complex graph structures, graph neural networks
(GNN) provide a generalized form of exploiting non-Euclidean space data. However, the …