The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …

Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network
(GNN), in which each node's representation is computed recursively by aggregating …

In graph classification, attention-and pooling-based graph neural networks (GNNs) prevail to
extract the critical features from the input graph and support the prediction. They mostly …

Graph representation learning has attracted increasing research attention. However, most
existing studies fuse all structural features and node attributes to provide an overarching …

A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

Social recommender systems are expected to improve recommendation quality by
incorporating social information when there is little user-item interaction data. However …

We consider feature representation learning problem of molecular graphs. Graph Neural
Networks have been widely used in feature representation learning of molecular graphs …

Graph kernels based on the $1 $-dimensional Weisfeiler-Leman algorithm and
corresponding neural architectures recently emerged as powerful tools for (supervised) …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …