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.

翻译：图神经网络已成为许多图级任务（如图分类和图生成）的主流架构。作为该架构的重要组成部分，图池化在获取整个图的全局图级表示中不可或缺。尽管这一前景广阔且快速发展的研究领域已涌现出大量方法，但据我们所知，目前鲜有工作对这些成果进行系统性总结。为奠定未来研究的基础，本文通过全面梳理近年来的图池化方法以填补这一空白。具体而言：1）我们首先提出现有图池化方法的分类体系，并对每类方法进行数学归纳；2）随后概述与图池化相关的资源库，包括常用数据集、下游任务的模型架构及开源实现；3）进一步归纳图池化方法在各领域中的应用；4）最后探讨当前研究面临的关键挑战，并就图池化改进的未来潜在研究方向提出见解。