  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 architecture, graph pooling is indispensable for
obtaining a holistic graph-level representation of the whole graph. Although a
great variety of methods have been proposed in this promising and
fast-developing research field, to the best of our knowledge, little effort has
been made to systematically summarize these works. To set the stage for the
development of future works, in this paper, we attempt to fill this gap by
providing a broad review of recent methods for graph pooling. Specifically, 1)
we first propose a taxonomy of existing graph pooling methods with a
mathematical summary for each category; 2) then, we provide an overview of the
libraries related to graph pooling, including the commonly used datasets, model
architectures for downstream tasks, and open-source implementations; 3) next,
we further outline the applications that incorporate the idea of graph pooling
in a variety of domains; 4) finally, we discuss certain critical challenges
facing current studies and share our insights on future potential directions
for research on the improvement of graph pooling.
