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 …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Lots of neural network architectures have been proposed to deal with learning tasks on
graph-structured data. However, most of these models concentrate on only node features …

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning
community in recent years. It has been widely adopted in various real-world applications …

Most state-of-the-art Graph Neural Networks focus on node features in the learning process
but ignore edge features. However, edge features also contain essential information in real …

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …

Existing Graph Neural Networks (GNNs) compute the message exchange between nodes
by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by …

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

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 …

As an efficient and scalable graph neural network, GraphSAGE has enabled an inductive
capability for inferring unseen nodes or graphs by aggregating subsampled local …