# Edge Contraction Pooling for Graph Neural Networks

arXiv: https://arxiv.org/abs/1905.10990 · HTML: https://mlsanity.com/paper/1905.10990/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 30 of 53)

- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [Accurate Learning of Graph Representations with Graph Multiset Pooling](https://mlsanity.com/paper/2102.11533/) (2021)
- [Understanding Pooling in Graph Neural Networks](https://mlsanity.com/paper/2110.05292/) (2021)
- [Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2204.07321/) (2022)
- [Edge Representation Learning with Hypergraphs](https://mlsanity.com/paper/2106.15845/) (2021)
- [The expressive power of pooling in Graph Neural Networks](https://mlsanity.com/paper/2304.01575/) (2023)
- [Contrastive Representation Learning for 3D Protein Structures](https://mlsanity.com/paper/2205.15675/) (2022)
- [Recognizing Predictive Substructures with Subgraph Information Bottleneck](https://mlsanity.com/paper/2103.11155/) (2021)
- [EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals](https://mlsanity.com/paper/2106.09135/) (2021)
- [Higher-order Clustering and Pooling for Graph Neural Networks](https://mlsanity.com/paper/2209.03473/) (2022)
- [Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs](https://mlsanity.com/paper/2203.16939/) (2022)
- [Graph Pooling via Coarsened Graph Infomax](https://mlsanity.com/paper/2105.01275/) (2021)
- [Hierarchical graph neural nets can capture long-range interactions](https://mlsanity.com/paper/2107.07432/) (2021)
- [Learning to Infer Kinematic Hierarchies for Novel Object Instances](https://mlsanity.com/paper/2110.07911/) (2021)
- [A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer](https://mlsanity.com/paper/2402.02464/) (2024)
- [Graph Neural Network-based Android Malware Classification with Jumping Knowledge](https://mlsanity.com/paper/2201.07537/) (2022)
- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability](https://mlsanity.com/paper/2406.09031/) (2024)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs](https://mlsanity.com/paper/2304.12217/) (2023)
- [MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks](https://mlsanity.com/paper/2409.05100/) (2024)
- [Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling](https://mlsanity.com/paper/2411.07482/) (2024)
- [Geometry-Aware Edge Pooling for Graph Neural Networks](https://mlsanity.com/paper/2506.11700/) (2025)
- [On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling](https://mlsanity.com/paper/2306.12726/) (2023)
- [Graph Parsing Networks](https://mlsanity.com/paper/2402.14393/) (2024)
- [Implementing graph neural networks with TensorFlow-Keras](https://mlsanity.com/paper/2103.04318/) (2021)
- [Edge but not Least: Cross-View Graph Pooling](https://mlsanity.com/paper/2109.11796/) (2021)
- [Approximate Network Motif Mining Via Graph Learning](https://mlsanity.com/paper/2206.01008/) (2022)
- [A Comparison of Graph Neural Networks for Malware Classification](https://mlsanity.com/paper/2303.12812/) (2023)
- [A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends](https://mlsanity.com/paper/2409.19629/) (2024)

## Abstract (arXiv)

Graph Neural Network (GNN) research has concentrated on improving convolutional layers, with little attention paid to developing graph pooling layers. Yet pooling layers can enable GNNs to reason over abstracted groups of nodes instead of single nodes. To close this gap, we propose a graph pooling layer relying on the notion of edge contraction: EdgePool learns a localized and sparse hard pooling transform. We show that EdgePool outperforms alternative pooling methods, can be easily integrated into most GNN models, and improves performance on both node and graph classification.