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) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …

While many systems have been developed to train graph neural networks (GNNs), efficient
model evaluation, which computes node embedding according to a given model, remains to …

Many Graph Neural Network (GNN) training systems have emerged recently to support
efficient GNN training. Since GNNs embody complex data dependencies between training …

Abstract Graph Neural Networks (GNNs) offer a compact and computationally efficient way
to learn embeddings and classifications on graph data. GNN models are frequently large …

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 …

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the
generative paradigm and learns to reconstruct masked graph edges or node features while …

GPUs are commonly utilized to accelerate GNN training, particularly on a multi-GPU server
with high-speed interconnects (eg, NVLink and NVSwitch). However, the rapidly increasing …

Graph Neural Networks (GNNs) have gained significant attention in recent years due to their
ability to learn representations of graph structured data. Two common methods for training …

Knowledge graph reasoning (KGR) answers logical queries over a knowledge graph (KG),
and embedding-based KGR (EKGR) becomes popular recently, which embeds both queries …