Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …

Graph convolutional networks (GCN) with one or two hidden layers have been widely used
in handling graph data that are prevalent in various disciplines. Many studies showed that …

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

Social networks are an essential component of the Internet of People (IoP) and play an
important role in stimulating interactive communication among people. Graph convolutional …

Graph representation learning is to learn universal node representations that preserve both
node attributes and structural information. The derived node representations can be used to …

Transfer learning on graphs drawn from varied distributions (domains) is in great demand
across many applications. Emerging methods attempt to learn domain-invariant …

Most graph neural networks follow the message passing mechanism. However, it faces the
over-smoothing problem when multiple times of message passing is applied to a graph …

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural
data has gained noticeable success in recent years. Nonetheless, most of the existing GCN …

Due to the powerful capability to gather the information of neighborhood nodes, Graph
Convolutional Network (GCN) has become a widely explored hotspot in recent years. As a …

Graph neural networks (GNNs) have achieved impressive impressions for graph-related
tasks. However, most GNNs are primarily studied under the cases of signal domain with …