mlsanityData to 14 Oct 2026

MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks

arXiv 2409.05100 ↗First posted 8 Sep 2024cs.LGcited by 5

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Abstract (arXiv)

We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach works well on any kind of graph topology and can find solutions that jointly optimize the MAXCUT along with other objectives. Based on the obtained MAXCUT partition, we implement a hierarchical graph pooling layer for Graph Neural Networks, which is sparse, trainable end-to-end, and particularly suitable for downstream tasks on heterophilic graphs.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 51 of its 73 references that are in the map. First posted on arXiv 8 Sep 2024.

Builds on

51 of 73 references.

References · 51

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018How Powerful are Graph Neural Networks?cited by 4,393
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  5. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
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  8. 2019Strategies for Pre-training Graph Neural Networkscited by 971
  9. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
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  11. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
  12. 2019Graph U-Netscited by 492
38 more
  1. 2019Self-Attention Graph Poolingcited by 421
  2. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 343
  3. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
  4. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  5. 2019Graph Neural Networks with convolutional ARMA filterscited by 185
  6. 2019Understanding Attention and Generalization in Graph Neural Networkscited by 169
  7. 2020Graph Clustering with Graph Neural Networkscited by 163
  8. 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
  9. 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 108
  10. 2019Hierarchical Graph Pooling with Structure Learningcited by 81
  11. 2021Understanding Pooling in Graph Neural Networkscited by 69
  12. 2022Structural Entropy Guided Graph Hierarchical Poolingcited by 62
  13. 2020Memory-Based Graph Networkscited by 56
  14. 2019Edge Contraction Pooling for Graph Neural Networkscited by 53
  15. 2020Topology-Aware Graph Pooling Networkscited by 44
  16. 2021AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filtercited by 43
  17. 2018Graph Coarsening with Preserved Spectral Propertiescited by 41
  18. 2021p-Laplacian Based Graph Neural Networkscited by 39
  19. 2020Path Integral Based Convolution and Pooling for Graph Neural Networkscited by 35
  20. 2023The expressive power of pooling in Graph Neural Networkscited by 35
  21. 2019Improving Graph Attention Networks with Large Margin-based Constraintscited by 34
  22. 2019Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Poolingcited by 30
  23. 2022Higher-order Clustering and Pooling for Graph Neural Networkscited by 26
  24. 2017Design of graph filters and filterbankscited by 22
  25. 2019iPool -- Information-based Pooling in Hierarchical Graph Neural Networkscited by 21
  26. 2019Towards Interpretable Sparse Graph Representation Learning with Laplacian Poolingcited by 20
  27. 2022Improving Graph Neural Networks with Learnable Propagation Operatorscited by 19
  28. 2023Graph-based Time Series Clustering for End-to-End Hierarchical Forecastingcited by 17
  29. 2024Graph-based Forecasting with Missing Data through Spatiotemporal Downsamplingcited by 17
  30. 2013A Multiscale Pyramid Transform for Graph Signalscited by 15
  31. 2021Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learningcited by 15
  32. 2021Graph Pooling via Coarsened Graph Infomaxcited by 15
  33. 2019Clique pooling for graph classificationcited by 13
  34. 2021Pyramidal Reservoir Graph Neural Networkcited by 10
  35. 2021RAMA: A Rapid Multicut Algorithm on GPUcited by 9
  36. 2022Simplifying Clustering with Graph Neural Networkscited by 8
  37. 2022Total Variation Graph Neural Networkscited by 7
  38. 2024A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizabilitycited by 7

50 most cited of 51 All 51 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Towards Efficient Training of Graph Neural Networks: A Multiscale Approachcited by 4
  2. 2025BN-Pool: Bayesian Nonparametric Pooling for Graphscited by 2
  3. 2025Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networkscited by 2
  4. 2026Neural QAOA2: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimizationcited by 1
  5. 2026Hierarchical Pooling for Sheaf Neural Networkscited by 0