  Recent years have witnessed the emergence and flourishing of hierarchical
graph pooling neural networks (HGPNNs) which are effective graph representation
learning approaches for graph level tasks such as graph classification.
However, current HGPNNs do not take full advantage of the graph's intrinsic
structures (e.g., community structure). Moreover, the pooling operations in
existing HGPNNs are difficult to be interpreted. In this paper, we propose a
new interpretable graph pooling framework - CommPOOL, that can capture and
preserve the hierarchical community structure of graphs in the graph
representation learning process. Specifically, the proposed community pooling
mechanism in CommPOOL utilizes an unsupervised approach for capturing the
inherent community structure of graphs in an interpretable manner. CommPOOL is
a general and flexible framework for hierarchical graph representation learning
that can further facilitate various graph-level tasks. Evaluations on five
public benchmark datasets and one synthetic dataset demonstrate the superior
performance of CommPOOL in graph representation learning for graph
classification compared to the state-of-the-art baseline methods, and its
effectiveness in capturing and preserving the community structure of graphs.
