Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks
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Abstract (arXiv)
Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernodes, how they handle batches, what they return after pooling, and whether they expose auxiliary losses. These differences make it hard to compare methods or reuse the same model code across them. tgp addresses this problem with a common software interface based on the Select-Reduce-Connect-Lift (SRCL) decomposition. The library provides 20 hierarchical poolers, standardized output objects, standalone readout modules, support for dense poolers in batched and unbatched mode, and workflows for caching and pre-coarsening. It is released under the MIT license on GitHub and PyPI, with comprehensive documentation, tutorials, and examples.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 46 references that are in the map. First posted on arXiv 14 Dec 2025.
Builds on
35 of 46 references.
References · 35
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
- 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
- 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
- 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
- 2019Graph U-Netscited by 492
- 2019Self-Attention Graph Poolingcited by 421
- 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 343
- 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
- 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
23 more
- 2022Long Range Graph Benchmarkcited by 263
- 2019Graph Neural Networks with convolutional ARMA filterscited by 185
- 2019Understanding Attention and Generalization in Graph Neural Networkscited by 169
- 2020Graph Clustering with Graph Neural Networkscited by 163
- 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
- 2018Contextual Stochastic Block Modelscited by 134
- 2019Graph Convolutional Networks with EigenPoolingcited by 94
- 2020Rethinking pooling in graph neural networkscited by 73
- 2021Understanding Pooling in Graph Neural Networkscited by 69
- 2022Structural Entropy Guided Graph Hierarchical Poolingcited by 62
- 2019Edge Contraction Pooling for Graph Neural Networkscited by 53
- 2020Path Integral Based Convolution and Pooling for Graph Neural Networkscited by 35
- 2023The expressive power of pooling in Graph Neural Networkscited by 35
- 2019Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Poolingcited by 30
- 2022Higher-order Clustering and Pooling for Graph Neural Networkscited by 26
- 2019Towards Interpretable Sparse Graph Representation Learning with Laplacian Poolingcited by 20
- 2019A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networkscited by 14
- 2021Pyramidal Reservoir Graph Neural Networkcited by 10
- 2022Simplifying Clustering with Graph Neural Networkscited by 8
- 2022Total Variation Graph Neural Networkscited by 7
- 2024MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networkscited by 5
- 2025Towards Efficient Training of Graph Neural Networks: A Multiscale Approachcited by 4
- 2025BN-Pool: Bayesian Nonparametric Pooling for Graphscited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.
- 2026Geometric Mean Pooling for Equal-Weight Multiplicative Coarse-Graining18 Sep 2026
- 2026Hierarchical Pooling for Sheaf Neural Networks18 Jun 2026