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 …

Abstract Heterophilous Graph Neural Network (GNN) is a family of GNNs that specializes in
learning graphs under heterophily, where connected nodes tend to have different labels …

Graph Neural Networks (GNNs) have emerged as powerful tools to capture structural
information from graph-structured data, achieving state-of-the-art performance on …

Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is
bandwidth-demanding and time-consuming. Frequent exchanges of node features …

Recently Graph Neural Networks (GNNs) have achieved great success in many
applications. The mini-batch training has become the de-facto way to train GNNs on giant …

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 …

Temporal Graph Neural Networks (TGNNs) extend the success of Graph Neural Networks to
dynamic graphs. Distributed TGNN training requires efficiently tackling temporal …

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

Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful
tool in many prediction tasks on graphs due to its advantages in model expressiveness and …

Dynamic graphs play a crucial role in various real-world applications, such as link prediction
and node classification on social media and e-commerce platforms. Temporal Graph Neural …