Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs
to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical …

Emerging graph neural networks (GNNs) have extended the successes of deep learning
techniques against datasets like images and texts to more complex graph-structured data …

Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is
a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

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 …

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

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 …

Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-
structured data, and training large-scale GCNs requires distributed training across multiple …