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

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

Many recent works in the field of graph machine learning have introduced pooling operators
to reduce the size of graphs. In this article, we present an operational framework to unify this …

Recent years have witnessed the emergence and flourishing of hierarchical graph pooling
neural networks (HGPNNs) which are effective graph representation learning approaches …

Recently, graph neural networks (GNNs) have revolutionized the field of graph
representation learning through effectively learned node embeddings, and achieved state-of …

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 …

Graph Neural Networks achieve state-of-the-art performance on a plethora of graph
classification tasks, especially due to pooling operators, which aggregate learned node …

Recent advances in representation learning on graphs, mainly leveraging graph
convolutional networks, have brought a substantial improvement on many graph-based …