Communication networks are important infrastructures in contemporary society. There are
still many challenges that are not fully solved and new solutions are proposed continuously …

Deep learning-based approaches have been developed to solve challenging problems in
wireless communications, leading to promising results. Early attempts adopted neural …

Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Graph neural networks (GNN) have shown outstanding applications in fields where data is
essentially represented as graphs (eg, chemistry, biology, and recommendation systems). In …

In recent years, with the rapid enhancement of computing power, deep learning methods
have been widely applied in wireless networks and achieved impressive performance. To …

Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

The advance of deep learning has shown great potential in applications (speech, image,
and video classification). In these applications, deep learning models are trained by …

Graph-structured data naturally appear in numerous application domains, ranging from
social analysis, bioinformatics to computer vision. The unique capability of graphs enables …

Abstract Graph Convolutional Networks (GCNs), aiming to obtain the representation of a
node by aggregating its neighbors, have demonstrated great power in tackling various …

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …