mlsanityData to 7 Oct 2026

Clique pooling for graph classification

arXiv 1904.00374 ↗First posted 31 Mar 2019cs.LG stat.MLcited by 13

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Abstract (arXiv)

We propose a novel graph pooling operation using cliques as the unit pool. As this approach is purely topological, rather than featural, it is more readily interpretable, a better analogue to image coarsening than filtering or pruning techniques, and entirely nonparametric. The operation is implemented within graph convolution network (GCN) and GraphSAGE architectures and tested against standard graph classification benchmarks. In addition, we explore the backwards compatibility of the pooling to regular graphs, demonstrating competitive performance when replacing two-by-two pooling in standard convolutional neural networks (CNNs) with our mechanism.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 4; 2024: 2.

13 papers.

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  1. 2021Understanding Pooling in Graph Neural Networkscited by 69
  2. 2022A Survey on Graph Representation Learning Methodscited by 66
  3. 2022Higher-order Clustering and Pooling for Graph Neural Networkscited by 26
  4. 2022MAgNET: A Graph U-Net Architecture for Mesh-Based Simulationscited by 26
  5. 2023Over-Squashing in Graph Neural Networks: A Comprehensive surveycited by 23
  6. 2021Structure-Aware Hierarchical Graph Pooling using Information Bottleneckcited by 6
  7. 2022Generalizing Downsampling from Regular Data to Graphscited by 6
  8. 2024Clustering for Protein Representation Learningcited by 6
  9. 2023Curvature-based Pooling within Graph Neural Networkscited by 5
  10. 2024MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networkscited by 5
  11. 2023Renormalized Graph Representations for Node Classificationcited by 1
  12. 2021Quadratic GCN for Graph Classificationcited by 0
1 more
  1. 2023NervePool: A Simplicial Pooling Layercited by 0