Following the success of convolution on non-Euclidean space, the corresponding pooling approaches have also been validated on various tasks regarding graphs. However, because of the fixed compression quota and stepwise pooling design, these hierarchical pooling methods still suffer from local structure damage and suboptimal problem. In this work, inspired by structural entropy, we propose a hierarchical pooling approach, SEP, to tackle the two issues. Specifically, without assigning the layer-specific compression quota, a global optimization algorithm is designed to generate the cluster assignment matrices for pooling at once. Then, we present an illustration of the local structure damage from previous methods in the reconstruction of ring and grid synthetic graphs. In addition to SEP, we further design two classification models, SEP-G and SEP-N for graph classification and node classification, respectively. The results show that SEP outperforms state-of-the-art graph pooling methods on graph classification benchmarks and obtains superior performance on node classifications.

本文提出了一种基于结构熵的分层池化方法，通过全局优化算法和集群分配矩阵生成来处理局部结构损失和亚优问题，同时进一步设计了两种分类模型：SEP-G和SEP-N，表明SEP在图分类基准测试中胜过目前最先进的图池化方法，并在节点分类上获得优越性能。

基于结构熵指导的图层次池化