Graph representation learning is a key technology for processing graph-structured data.
Graph convolutional networks (GCNs), as a type of currently emerging and commonly used …

Graph convolutional network (GCN) is an effective neural network model for graph
representation learning. However, standard GCN suffers from three main limitations:(1) most …

Graph representation learning is of paramount importance for a variety of graph analytical
tasks, ranging from node classification to community detection. Recently, graph …

Graph convolutional networks (GCNs) have been widely used for representation learning on
graph data, which can capture structural patterns on a graph via specifically designed …

Graph neural networks (GNNs) have emerged as a powerful tool in graph representation
learning. However, they are increasingly challenged by over-smoothing as network depth …

Graph convolutional networks have achieved considerable success in various graph
domain tasks. Recently, numerous types of graph convolutional networks have been …

Graph neural networks have shown significant success in the field of graph representation
learning. Graph convolutions perform neighborhood aggregation and represent one of the …

Graph Convolutional Networks (GCNs) are powerful representation learning methods for
non-Euclidean data. Compared with the Euclidean data, labeling the non-Euclidean data is …

Graph convolutional networks (GCNs) have emerged as powerful tools for learning
representations of graph structured data. A recent advancement is a decoupled GCN, which …

Graph neural networks (GNNs) have significantly improved the representation power for
graph-structured data. Despite of the recent success of GNNs, the graph convolution in most …