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 …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

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 …

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 …

We propose GNNLab, a sample-based GNN training system in a single machine multi-GPU
setup. GNNLab adopts a factored design for multiple GPUs, where each GPU is dedicated to …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs)
to non-Euclidean graph data, achieving ground-breaking performance on various tasks such …

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 neural network (GNN) has been widely applied in real-world applications, such as
product recommendation in e-commerce platforms and risk control in financial management …