In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. To this end, we commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs. We individually employ a local graph convolution units as the local structure to further compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. Since these subgraphs are separated by different clusters and the structural information cannot be propagated between them, the local convolution operation can significantly avoid the over-smoothing problem arising in most existing Graph Neural Networks (GNNs). By hierarchically performing the proposed procedures on the resulting coarsened graph, the proposed SSHPool can effectively extract the hierarchical global feature of the original graph structure, encapsulating rich intrinsic structural characteristics. Furthermore, we develop an end-to-end GNN framework associated with the proposed SSHPool module for graph classification. Experimental results demonstrate the superior performance of the proposed model on real-world datasets, significantly outperforming state-of-the-art GNN methods in terms of the classification accuracies.

通过本研究，我们提出了一种名为Separated Subgraph-based Hierarchical Pooling (SSHPool)的新型局部图池化方法，通过分配样本图中的节点到不同聚类中从而得到一组分离的子图，然后利用局部图卷积单元将每个子图进一步压缩为粗化节点，将原始图转化为粗化图。通过层级执行这些过程，SSHPool可以从原始图结构中有效提取层级全局特征，包含丰富的内在结构特征。通过与SSHPool模块相关的端到端GNN框架进行实验，结果表明所提出模型在真实数据集上表现优异，以分类准确度而言明显优于最先进的GNN方法。

SSHPool：基于分离子图的分层池化