# CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning

arXiv: https://arxiv.org/abs/2012.05980 · HTML: https://mlsanity.com/paper/2012.05980/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2204.07321/) (2022)
- [Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model](https://mlsanity.com/paper/2207.07650/) (2022)
- [On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling](https://mlsanity.com/paper/2306.12726/) (2023)
- [PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images](https://mlsanity.com/paper/2108.03809/) (2021)
- [Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing](https://mlsanity.com/paper/2404.00589/) (2024)
- [A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases](https://mlsanity.com/paper/2412.06212/) (2024)

## Abstract (arXiv)

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.