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 …

Improving the training and inference performance of graph neural networks (GNNs) is faced
with a challenge uncommon in general neural networks: creating mini-batches requires a lot …

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 …

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 attention models (A-GNNs), a type of Graph Neural Networks (GNNs), have been
shown to be more powerful than simpler convolutional GNNs (C-GNNs). However, A-GNNs …

Recent advances in Graph Neural Networks (GNNs) have changed the landscape of
modern graph analytics. The complexity of GNN training and the scalability challenges have …

As Graph Neural Networks (GNNs) become popular, libraries like PyTorch-Geometric (PyG)
and Deep Graph Library (DGL) are proposed; these libraries have emerged as the de facto …

Graph neural networks (GNNs) are a type of neural network capable of learning on graph-
structured data. However, training GNNs on large-scale graphs is challenging due to …

Abstract Artificial Intelligence (AI) and Deep Learning (DL) have gained enormous popularity
and have seen wide adoption across different domains. They ushered in an era of huge …