The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new
architectures as well as novel applications. However, current research focuses on proposing …

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

Message-passing neural networks (MPNNs) have been successfully applied to
representation learning on graphs in a variety of real-world applications. However, two …

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

Graph Convolutional Networks (GCNs) have gained great popularity in tackling various
analytics tasks on graph and network data. However, some recent studies raise concerns …

Graph neural networks (GNN) have shown great success in learning from graph-structured
data. They are widely used in various applications, such as recommendation, fraud …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Learning node embeddings that capture a node's position within the broader graph structure
is crucial for many prediction tasks on graphs. However, existing Graph Neural Network …

Graph Neural Network (GNN) has been demonstrated its effectiveness in dealing with non-
Euclidean structural data. Both spatial-based and spectral-based GNNs are relying on …

Abstract Graph Neural Networks (GNNs) have been studied through the lens of expressive
power and generalization. However, their optimization properties are less well understood …