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 …

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 breakthrough performance in graph analytics
such as node classification, link prediction and graph clustering. Many GNN training …

Graph Neural Network (GNN) has recently drawn a rapid increase of interest in many
domains for its effectiveness in learning over graphs. Maximizing its performance is …

Abstract Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art
method for graph-based learning tasks. However, training GCNs at scale is still challenging …

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) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …

Graph convolutional networks (GCNs) have received significant attention from various
research fields due to the excellent performance in learning graph representations. Although …

Training Graph Neural Networks (GNNs) on large graphs is a fundamental challenge due to
the high memory usage, which is mainly occupied by activations (eg, node embeddings) …

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 …