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

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

The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

GNN's training needs to resolve issues of vertex dependencies, ie, each vertex
representation's update depends on its neighbors. Existing distributed GNN systems adopt …

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 …

Abstract Graph Neural Networks (GNNs) have been widely used in various domains, and
GNNs with sophisticated computational graph lead to higher latency and larger memory …

Training and inference with graph neural networks (GNNs) on massive graphs in a
distributed environment has been actively studied since the inception of GNNs, owing to the …

Graph neural networks (GNNs) have been a hot spot of recent research and are widely
utilized in diverse applications. However, with the use of huger data and deeper models, an …