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 …

Although there exist several libraries for deep learning on graphs, they are aiming at
implementing basic operations for graph deep learning. In the research community …

Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard
plights in training deep architectures such as vanishing gradients and overfitting, it also …

Graph neural networks (GNNs) have achieved outstanding performance in learning graph-
structured data and various tasks. However, many current GNNs suffer from three common …

Graph neural networks (GNNs) have achieved great success in many graph-based
applications. However, the enormous size and high sparsity level of graphs hinder their …

Recently, there has been an increasing interest in (supervised) learning with graph data,
especially using graph neural networks. However, the development of meaningful …

Graph Neural Nets (GNNs) have received increasing attentions, partially due to their
superior performance in many node and graph classification tasks. However, there is a lack …

Graph neural networks (GNN) have shown great success in learn-ing from graph-structured
data. They are widely used in various applications, such as recommendation, fraud …

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 …

Graph neural networks have achieved state-of-the-art performance on graph-related tasks.
Previous methods observed that GNNs' performance degrades as the number of layers …