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 …

The graph Laplacian regularization term is usually used in semi-supervised representation
learning to provide graph structure information for a model f (X). However, with the recent …

Graph neural networks (GNNs) have limited expressive power, failing to represent many
graph classes correctly. While more expressive graph representation learning (GRL) …

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 …

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

Graph neural networks (GNNs) have received tremendous attention due to their superiority
in learning node representations. These models rely on message passing and feature …

Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
However, there is relatively little understanding of why GNNs are successful in practice and …

Graph neural networks (GNNs) emerge as a powerful family of representation learning
models on graphs. To derive node representations, they utilize a global model that …

Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

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 …