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.

近年来，分层图池化神经网络 (HGPNN) 的出现和蓬勃发展，它们是图分类等图级任务的有效图表示学习方法。然而，当前的 HGPNNs 没有充分利用图的内在结构（例如，社区结构）。此外，现有 HGPNN 中的池化操作难以解释。在本文中，我们提出了一种新的可解释图池化框架——CommPOOL，它可以在图表示学习过程中捕获和保留图的分层社区结构。具体而言，CommPOOL 中提议的社区池机制利用无监督方法以可解释的方式捕获图的固有社区结构。CommPOOL 是一个通用且灵活的分层图表示学习框架，可以进一步促进各种图级任务。对五个公共基准数据集和一个合成数据集的评估表明，与最先进的基线方法相比，CommPOOL 在图分类的图表示学习方面具有卓越的性能，以及它在捕获和保留图的社区结构方面的有效性。