mlsanityData to 14 Oct 2026

Structural Entropy Guided Graph Hierarchical Pooling

arXiv 2206.13510 ↗First posted 26 Jun 2022cs.LGcited by 62

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Abstract (arXiv)

Following the success of convolution on non-Euclidean space, the corresponding pooling approaches have also been validated on various tasks regarding graphs. However, because of the fixed compression quota and stepwise pooling design, these hierarchical pooling methods still suffer from local structure damage and suboptimal problem. In this work, inspired by structural entropy, we propose a hierarchical pooling approach, SEP, to tackle the two issues. Specifically, without assigning the layer-specific compression quota, a global optimization algorithm is designed to generate the cluster assignment matrices for pooling at once. Then, we present an illustration of the local structure damage from previous methods in the reconstruction of ring and grid synthetic graphs. In addition to SEP, we further design two classification models, SEP-G and SEP-N for graph classification and node classification, respectively. The results show that SEP outperforms state-of-the-art graph pooling methods on graph classification benchmarks and obtains superior performance on node classifications.

Key facts. Cited by 62 papers in the map (data through 14 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 26 Jun 2022.

Builds on

23 of 36 references.

References · 23

  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. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  6. 2019Simplifying Graph Convolutional Networkscited by 1,477
  7. 2018Deep Graph Infomaxcited by 1,106
  8. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  9. 2020Simple and Deep Graph Convolutional Networkscited by 860
  10. 2018Link Prediction Based on Graph Neural Networkscited by 832
  11. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  12. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
11 more
  1. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  2. 2019Graph U-Netscited by 492
  3. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 443
  4. 2019Self-Attention Graph Poolingcited by 421
  5. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  6. 2015Order Matters: Sequence to sequence for setscited by 237
  7. 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
  8. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  9. 2019Graph Convolutional Networks with EigenPoolingcited by 94
  10. 2020Rethinking pooling in graph neural networkscited by 73
  11. 2022A Simple yet Effective Method for Graph Classificationcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 17; 2024: 23; 2025: 14; 2026: 7.

62 papers.

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  1. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  2. 2023SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimizationcited by 35
  3. 2024Accelerating Transformers with Spectrum-Preserving Token Mergingcited by 35
  4. 2022Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networkscited by 24
  5. 2024LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clusteringcited by 21
  6. 2024EXGC: Bridging Efficiency and Explainability in Graph Condensationcited by 20
  7. 2023Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detectioncited by 19
  8. 2023HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classificationcited by 17
  9. 2024Subgraph Pooling: Tackling Negative Transfer on Graphscited by 17
  10. 2024SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detectioncited by 16
  11. 2023Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detectioncited by 15
  12. 2023Hierarchical State Abstraction Based on Structural Information Principlescited by 14
38 more
  1. 2023MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimizationcited by 13
  2. 2023The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive fieldcited by 11
  3. 2024Graph Classification via Reference Distribution Learning: Theory and Practicecited by 11
  4. 2024Towards Dynamic Message Passing on Graphscited by 11
  5. 2023Adversarial Socialbots Modeling Based on Structural Information Principlescited by 10
  6. 2023Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Trainingcited by 9
  7. 2024Boosting Graph Pooling with Persistent Homologycited by 9
  8. 2024Cluster-wise Graph Transformer with Dual-granularity Kernelized Attentioncited by 8
  9. 2023Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphscited by 7
  10. 2024A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizabilitycited by 7
  11. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  12. 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learningcited by 6
  13. 2024Learning to Approximate Adaptive Kernel Convolution on Graphscited by 6
  14. 2024MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networkscited by 5
  15. 2023Feudal Graph Reinforcement Learningcited by 4
  16. 2023Patch-wise Graph Contrastive Learning for Image Translationcited by 4
  17. 2024Graph Parsing Networkscited by 4
  18. 2024Effective Exploration Based on the Structural Information Principlescited by 4
  19. 2024Is Less More? Exploring Token Condensation as Training-free Test-time Adaptationcited by 4
  20. 2025ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Spacecited by 4
  21. 2025Unsupervised Graph Clustering with Deep Structural Entropycited by 4
  22. 2026KAT-Coder-V2.5 Technical Reportcited by 4
  23. 2024Heterogeneous Interaction Modeling With Reduced Accumulated Error for Multi-Agent Trajectory Predictioncited by 3
  24. 2024Rumor Detection on Social Media with Temporal Propagation Structure Optimizationcited by 3
  25. 2025Applications of Entropy in Data Analysis and Machine Learning: A Reviewcited by 3
  26. 2025Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detectioncited by 3
  27. 2025Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learningcited by 3
  28. 2025Hyperbolic Continuous Structural Entropy for Hierarchical Clusteringcited by 3
  29. 2024Graph Learning with Distributional Edge Layoutscited by 2
  30. 2024Scalable Graph Compressed Convolutionscited by 2
  31. 2025BN-Pool: Bayesian Nonparametric Pooling for Graphscited by 2
  32. 2025Efficient Curvature-aware Graph Networkcited by 2
  33. 2025Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networkscited by 2
  34. 2023Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodescited by 1
  35. 2024SSHPool: The Separated Subgraph-based Hierarchical Poolingcited by 1
  36. 2024ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networkscited by 1
  37. 2025Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learningcited by 1
  38. 2025Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passingcited by 1

50 most cited of 62 All 62 (JSON)