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Edge Contraction Pooling for Graph Neural Networks

arXiv 1905.10990 ↗First posted 27 May 2019cs.LG stat.MLcited by 53

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 13; 2022: 11; 2023: 7; 2024: 11; 2025: 7; 2026: 4.

53 papers.

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  1. 2022Graph neural networks for materials science and chemistrycited by 182
  2. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  3. 2021Understanding Pooling in Graph Neural Networkscited by 69
  4. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  5. 2021Edge Representation Learning with Hypergraphscited by 37
  6. 2023The expressive power of pooling in Graph Neural Networkscited by 35
  7. 2022Contrastive Representation Learning for 3D Protein Structurescited by 30
  8. 2021Recognizing Predictive Substructures with Subgraph Information Bottleneckcited by 28
  9. 2021EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signalscited by 28
  10. 2022Higher-order Clustering and Pooling for Graph Neural Networkscited by 26
  11. 2022Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphscited by 16
  12. 2021Graph Pooling via Coarsened Graph Infomaxcited by 15
38 more
  1. 2021Hierarchical graph neural nets can capture long-range interactionscited by 12
  2. 2021Learning to Infer Kinematic Hierarchies for Novel Object Instancescited by 10
  3. 2024A Graph is Worth K Words: Euclideanizing Graph using Pure Transformercited by 10
  4. 2022Graph Neural Network-based Android Malware Classification with Jumping Knowledgecited by 9
  5. 2022Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applicationscited by 7
  6. 2024A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizabilitycited by 7
  7. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  8. 2023Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphscited by 5
  9. 2024MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networkscited by 5
  10. 2024Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Samplingcited by 5
  11. 2025Geometry-Aware Edge Pooling for Graph Neural Networkscited by 5
  12. 2023On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Poolingcited by 4
  13. 2024Graph Parsing Networkscited by 4
  14. 2021Implementing graph neural networks with TensorFlow-Kerascited by 3
  15. 2021Edge but not Least: Cross-View Graph Poolingcited by 3
  16. 2022Approximate Network Motif Mining Via Graph Learningcited by 3
  17. 2023A Comparison of Graph Neural Networks for Malware Classificationcited by 3
  18. 2024A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trendscited by 3
  19. 2021Detail-aware Deep Clothing Animations Infused with Multi-source Attributescited by 2
  20. 2022Embedding Graphs on Grassmann Manifoldcited by 2
  21. 2025BN-Pool: Bayesian Nonparametric Pooling for Graphscited by 2
  22. 2025Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networkscited by 2
  23. 2026Hierarchical Discrete Flow Matching for Graph Generationcited by 2
  24. 2023Edge-aware Hard Clustering Graph Pooling for Brain Imagingcited by 1
  25. 2023Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodescited by 1
  26. 2024Motif-driven Subgraph Structure Learning for Graph Classificationcited by 1
  27. 2024Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphscited by 1
  28. 2024MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Lengthcited by 1
  29. 2026The Role of Node Features in Graph Poolingcited by 1
  30. 2021Automatic design of novel potential 3CLpro and PLpro inhibitorscited by 0
  31. 2021Quadratic GCN for Graph Classificationcited by 0
  32. 2022Memory Efficient Temporal & Visual Graph Model for Unsupervised Video Domain Adaptationcited by 0
  33. 2022Efficient Automatic Machine Learning via Design Graphscited by 0
  34. 2024Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillancecited by 0
  35. 2024Estimation of FFR in coronary arteries with deep learningcited by 0
  36. 2025LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisationcited by 0
  37. 2025Are Graph Attention Networks Able to Model Structural Information?cited by 0
  38. 2025Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learningcited by 0

50 most cited of 53 All 53 (JSON)