Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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 …

Advancing research in the emerging field of deep graph learning requires new tools to
support tensor computation over graphs. In this paper, we present the design principles and …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

Advancing research in the emerging field of deep graph learning requires new tools to
support tensor computation over graphs. In this paper, we present the design principles and …

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 …

A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

Graph Neural Networks (GNNs) have gained significant attention in the recent past, and
become one of the fastest growing subareas in deep learning. While several new GNN …

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 …

Graph neural networks (GNNs) have shown excellent performance in a wide range of
applications such as recommendation, risk control, and drug discovery. With the increase in …