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SpaPool: Soft Partition Assignment Pooling for__Graph Neural Networks

arXiv 2509.11675 ↗First posted 15 Sep 2025stat.ML cs.LGcited by 0

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Abstract (arXiv)

This paper introduces SpaPool, a novel pooling method that combines the strengths of both dense and sparse techniques for a graph neural network. SpaPool groups vertices into an adaptive number of clusters, leveraging the benefits of both dense and sparse approaches. It aims to maintain the structural integrity of the graph while reducing its size efficiently. Experimental results on several datasets demonstrate that SpaPool achieves competitive performance compared to existing pooling techniques and excels particularly on small-scale graphs. This makes SpaPool a promising method for applications requiring efficient and effective graph processing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 18 references that are in the map. First posted on arXiv 15 Sep 2025.

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References · 16

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  6. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  7. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
  8. 2019Graph U-Netscited by 492
  9. 2019Self-Attention Graph Poolingcited by 421
  10. 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 205
  11. 2020Graph Clustering with Graph Neural Networkscited by 163
  12. 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
4 more
  1. 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classificationcited by 85
  2. 2022A Short Tutorial on The Weisfeiler-Lehman Test And Its Variantscited by 52
  3. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  4. 2019Towards Interpretable Sparse Graph Representation Learning with Laplacian Poolingcited by 20

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