Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured
data, have drawn considerable attention and achieved state-of-the-art performance in …

Graph Neural Networks (GNNs), whch generalize deep neural networks to graph-structured
data, have drawn considerable attention and achieved state-of-the-art performance in …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

In graph neural networks (GNNs), pooling operators compute local summaries of input
graphs to capture their global properties, and they are fundamental for building deep GNNs …

Graph neural networks have achieved great success in learning node representations for
graph tasks such as node classification and link prediction. Graph representation learning …

Graph neural networks, which generalize deep neural network models to graph structured
data, have attracted increasing attention in recent years. They usually learn node …

Graph Neural Network (GNN) research has concentrated on improving convolutional layers,
with little attention paid to developing graph pooling layers. Yet pooling layers can enable …

Graph neural networks have been widely used on modeling graph data, achieving
impressive results on node classification and link prediction tasks. Yet, obtaining an …

Graph neural networks (GNNs) have been widely used to learn vector representation of
graph-structured data and achieved better task performance than conventional methods …

Graph convolutional networks (GCNs) are potentially short of the ability to learn hierarchical
representation for graph embedding, which holds them back in the graph classification task …