Convolutional Neural Networks (CNNs) have enabled major advances in image
classification through convolution and pooling. In particular, image pooling
transforms a connected discrete lattice into a reduced lattice with the same
connectivity and allows reduction functions to consider all pixels in an image.
However, there is no pooling that satisfies these properties for graphs. In
fact, traditional graph pooling methods suffer from at least one of the
following drawbacks: Graph disconnection or overconnection, low decimation
ratio, and deletion of large parts of graphs. In this paper, we present three
pooling methods based on the notion of maximal independent sets that avoid
these pitfalls. Our experimental results confirm the relevance of maximal
independent set constraints for graph pooling.

在图像分类中，卷积神经网络通过卷积和池化实现了重大进展；然而，对于图中的池化方法存在着一些问题，如图的断开或过连接、低减少率和删除大部分图等。本文基于最大独立集的概念提出了三种池化方法，并通过实验结果确认最大独立集约束对图池化的相关性。