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

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

The mining and exploitation of graph structural information have been the focal points in the
study of complex networks. Traditional structural measures in Network Science focus on the …

Half a decade of graph convolutional networks | Nature Machine Intelligence Skip to main
content Thank you for visiting nature.com. You are using a browser version with limited support …

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 representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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 …

Convolutional neural networks (CNNs) leverage the great power in representation learning
on regular grid data such as image and video. Recently, increasing attention has been paid …

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-
structured data. Recently, GCNs and subsequent variants have shown superior performance …

The last half-decade has seen a surge in deep learning research on irregular domains and
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …