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

Graph neural networks (GNNs) are effective machine learning models for various graph
learning problems. Despite their empirical successes, the theoretical limitations of GNNs …

Since the proposal of the graph neural network (GNN) by Gori et al.(2005) and Scarselli et
al.(2008), one of the major problems in training GNNs was their struggle to propagate …

Graph neural networks (GNNs) are widely used in machine learning for graph-structured
data. Even though GNNs have achieved remarkable success in real-world applications …

Abstract Graph Neural Networks (GNNs) are limited in their propagation operators. In many
cases, these operators often contain non-negative elements only and are shared across …

Training graph neural networks (GNNs) on large graphs is complex and extremely time
consuming. This is attributed to overheads caused by sparse matrix multiplication, which are …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

Deep graph neural networks (GNNs) have achieved excellent results on various tasks on
increasingly large graph datasets with millions of nodes and edges. However, memory …

Graph neural networks (GNNs), as the de-facto model class for representation learning on
graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional …

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …