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 …

Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large
graphs remains challenging. The limited resource capacities of the existing servers, the …

Graph summarization as a preprocessing step is an effective and complementary technique
for scalable graph neural network (GNN) training. In this work, we propose the Coarsening …

In recent years, distributed graph convolutional networks (GCNs) training frameworks have
achieved great success in learning the representation of graph-structured data with large …

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 …

Distributed graph neural network (GNN) training facilitates learning on massive graphs that
surpass the storage and computational capabilities of a single machine. Traditional …

Graph convolutional networks (GCNs) have been widely adopted for graph representation
learning and achieved impressive performance. For larger graphs stored separately on …

Graph Neural Networks (GNNs) have demonstrated significant potential in extending the
empirical success of deep learning from Euclidean spaces to non-Euclidean, graph …