SSHPool: The Separated Subgraph-based Hierarchical Pooling
Zhuo Xu Lixin Cui Yue Wang Hangyuan Du Lu Bai Edwin R. Hancock
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.