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 …

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad
applicability to graph-related problems such as quantum chemistry, drug discovery, and high …

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 …

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning
model. However, it can be notoriously challenging to inference GCNs over large graph …

Graph convolutional networks (GCNs) have been introduced to effectively process non-
Euclidean graph data. However, GCNs incur large amounts of irregularity in computation …

Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their
success in learning representations from graph-structured data across several critical …

As graph neural networks (GNNs) have achieved great success in many graph learning
problems, it is of paramount importance to support their efficient execution. Different graphs …

In recent times, a plethora of hardware accelerators have been put forth for graph learning
applications such as vertex classification and graph classification. However, previous works …

Graph Neural Networks (GNNs) have shown success in many real-world applications that
involve graph-structured data. Most of the existing single-node GNN training systems are …