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 …

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 …

Heterogeneous Graph Neural Networks (HGNNs) leverage diverse semantic relationships
in Heterogeneous Graphs (HetGs) and have demonstrated remarkable learning …

Graph Neural Networks (GNN) are indispensable in learning from graph-structured data, yet
their rising computational costs, especially on massively connected graphs, pose significant …

Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to
the large memory and computing requirements. Distributed GNN training, where the graph is …

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 …

Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning.
While many distributed GNN training frameworks have been proposed to increase the …

Graph Convolutional Networks (GCNs) are widely used in various domains. However,
training distributed full-batch GCNs on large-scale graphs poses challenges due to …

Graph neural networks (GNNs) have emerged as a promising direction. Training large-scale
graphs that relies on distributed computing power poses new challenges. Existing …