# Hierarchical Message-Passing Graph Neural Networks

arXiv: https://arxiv.org/abs/2009.03717 · HTML: https://mlsanity.com/paper/2009.03717/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 21 of 21)

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- [Improving the Effective Receptive Field of Message-Passing Neural Networks](https://mlsanity.com/paper/2505.23185/) (2025)
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- [SMGRL: Scalable Multi-resolution Graph Representation Learning](https://mlsanity.com/paper/2201.12670/) (2022)
- [Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach](https://mlsanity.com/paper/2201.09086/) (2022)
- [Topological Feature Compression for Molecular Graph Neural Networks](https://mlsanity.com/paper/2508.07807/) (2025)
- [Graph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling for Strategic Multiagent Settings](https://mlsanity.com/paper/2511.10501/) (2025)
- [Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning](https://mlsanity.com/paper/2605.21475/) (2026)
- [Periodic Topological Deep Learning for Polymer Design and Discovery](https://mlsanity.com/paper/2605.26833/) (2026)
- [Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets](https://mlsanity.com/paper/2606.09051/) (2026)
- [CoRe-GNN: Multilevel Message passing on Coarsened graphs](https://mlsanity.com/paper/2608.02128/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have become a prominent approach to machine learning with graphs and have been increasingly applied in a multitude of domains. Nevertheless, since most existing GNN models are based on flat message-passing mechanisms, two limitations need to be tackled: (i) they are costly in encoding long-range information spanning the graph structure; (ii) they are failing to encode features in the high-order neighbourhood in the graphs as they only perform information aggregation across the observed edges in the original graph. To deal with these two issues, we propose a novel Hierarchical Message-passing Graph Neural Networks framework. The key idea is generating a hierarchical structure that re-organises all nodes in a flat graph into multi-level super graphs, along with innovative intra- and inter-level propagation manners. The derived hierarchy creates shortcuts connecting far-away nodes so that informative long-range interactions can be efficiently accessed via message passing and incorporates meso- and macro-level semantics into the learned node representations. We present the first model to implement this framework, termed Hierarchical Community-aware Graph Neural Network (HC-GNN), with the assistance of a hierarchical community detection algorithm. The theoretical analysis illustrates HC-GNN's remarkable capacity in capturing long-range information without introducing heavy additional computation complexity. Empirical experiments conducted on 9 datasets under transductive, inductive, and few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN models in network analysis tasks, including node classification, link prediction, and community detection. Moreover, the model analysis further demonstrates HC-GNN's robustness facing graph sparsity and the flexibility in incorporating different GNN encoders.