Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning
algorithms to train on non-euclidean data. GNNs are widely used in recommender systems …

As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel
for their capability to generate high-quality node feature vectors (embeddings). However, the …

Graph Neural Network (GNN) has recently drawn a rapid increase of interest in many
domains for its effectiveness in learning over graphs. Maximizing its performance is …

Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-
structured data and performing sophisticated inference tasks in various application domains …

With the increasing adoption of graph neural networks (GNNs) in the machine learning
community, GPUs have become an essential tool to accelerate GNN training. However …

The Graph Neural Network (GNN) is showing outstanding results in improving the
performance of graph-based applications. Recent studies demonstrate that GNN …

This paper demonstrates G3, a framework for Graph Neural Network (GNN) training, tailored
from Graph processing systems on Graphics processing units (GPUs). G3 aims at improving …

Graph neural networks (GNNs) have been emerging as powerful learning tools for
unstructured data and successfully applied to many graph-based application domains …

Full-graph training on graph neural networks (GNN) has emerged as a promising training
method for its effectiveness. Full-graph training requires extensive memory and computation …

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 …