Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

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 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 (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Recent works have demonstrated the benefits of capturing long-distance dependency in
graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long …

Training of graph neural networks (GNNs) for large-scale node classification is challenging.
A key difficulty lies in obtaining accurate hidden node representations while avoiding the …

Training graph neural networks (GNNs) is extremely time consuming because sparse graph-
based operations are hard to be accelerated by community hardware. Prior art successfully …

Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …

Graph Neural Networks (GNNs) have emerged as powerful tools to capture structural
information from graph-structured data, achieving state-of-the-art performance on …

Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is
bandwidth-demanding and time-consuming. Frequent exchanges of node features …