mlsanityData to 7 Oct 2026

SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism

arXiv 2101.08170 ↗First posted 20 Jan 2021cs.LG cs.AIcited by 75

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Abstract (arXiv)

Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching view of graphs, neglecting finer substructures' semantics, and suffering from interpretation enigmas. This paper presents a novel hierarchical subgraph-level selection and embedding based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way. SUGAR reconstructs a sketched graph by extracting striking subgraphs as the representative part of the original graph to reveal subgraph-level patterns. To adaptively select striking subgraphs without prior knowledge, we develop a reinforcement pooling mechanism, which improves the generalization ability of the model. To differentiate subgraph representations among graphs, we present a self-supervised mutual information mechanism to encourage subgraph embedding to be mindful of the global graph structural properties by maximizing their mutual information. Extensive experiments on six typical bioinformatics datasets demonstrate a significant and consistent improvement in model quality with competitive performance and interpretability.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). Builds on 42 of its 64 references that are in the map. First posted on arXiv 20 Jan 2021.

Builds on

42 of 64 references.

References · 42

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30 more
  1. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  2. 2015Gated Graph Sequence Neural Networkscited by 792
  3. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  4. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
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  6. 2019Graph U-Netscited by 489
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  8. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  9. 2019Self-Attention Graph Poolingcited by 421
  10. 2018Graph Convolutional Networks for Text Classificationcited by 345
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  12. 2020Improving Graph Neural Network Expressivity via Subgraph Isomorphism Countingcited by 312
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  16. 2020Graph Representation Learning via Graphical Mutual Information Maximizationcited by 217
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  18. 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
  19. 2018Adaptive Graph Convolutional Neural Networkscited by 155
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  21. 2018Towards Sparse Hierarchical Graph Classifierscited by 100
  22. 2019Graph Convolutional Networks with EigenPoolingcited by 94
  23. 2020Subgraph Neural Networkscited by 73
  24. 2018MotifNet: a motif-based Graph Convolutional Network for directed graphscited by 63
  25. 2019Semi-Supervised Graph Classification: A Hierarchical Graph Perspectivecited by 55
  26. 2018Anonymous Walk Embeddingscited by 46
  27. 2018Graph Capsule Convolutional Neural Networkscited by 27
  28. 2018Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoderscited by 17
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 18; 2023: 17; 2024: 16; 2025: 8; 2026: 2.

75 papers.

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  1. 2021Graph Self-Supervised Learning: A Surveycited by 289
  2. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  3. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  4. 2021Augmentation-Free Self-Supervised Learning on Graphscited by 94
  5. 2021Graph Structure Learning with Variational Information Bottleneckcited by 81
  6. 2023Does Graph Distillation See Like Vision Dataset Counterpart?cited by 45
  7. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  8. 2024A Comprehensive Survey on Data Augmentationcited by 38
  9. 2021Prototypical Graph Contrastive Learningcited by 37
  10. 2023Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Modelscited by 37
  11. 2022MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformercited by 35
  12. 2022Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashingcited by 31
38 more
  1. 2022Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphscited by 26
  2. 2023Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalizationcited by 26
  3. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26
  4. 2021ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Networkcited by 24
  5. 2023Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classificationcited by 23
  6. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  7. 2022Graph Self-supervised Learning with Accurate Discrepancy Learningcited by 21
  8. 2022Reinforcement learning on graphs: A surveycited by 18
  9. 2023From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networkscited by 17
  10. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  11. 2021Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networkscited by 13
  12. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  13. 2021A Survey on Deep Learning Event Extraction: Approaches and Applicationscited by 12
  14. 2022Deep reinforcement learning guided graph neural networks for brain network analysiscited by 12
  15. 2023MAG-GNN: Reinforcement Learning Boosted Graph Neural Networkcited by 11
  16. 2022Data Augmentation on Graphs: A Technical Surveycited by 10
  17. 2022Curvature Graph Generative Adversarial Networkscited by 9
  18. 2024RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuningcited by 9
  19. 2022Self-organization Preserved Graph Structure Learning with Principle of Relevant Informationcited by 8
  20. 2024GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptationcited by 8
  21. 2022SHINE: SubHypergraph Inductive Neural nEtworkcited by 7
  22. 2023Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detectioncited by 7
  23. 2023Poincar'e Differential Privacy for Hierarchy-Aware Graph Embeddingcited by 7
  24. 2022Heterogeneous Graph Neural Network for Privacy-Preserving Recommendationcited by 6
  25. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  26. 20233D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Informationcited by 6
  27. 2023Contrastive Graph Pooling for Explainable Classification of Brain Networkscited by 5
  28. 2023Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learningcited by 5
  29. 2024Dynamic Graph Information Bottleneckcited by 5
  30. 2024Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Datacited by 5
  31. 2025Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centralitycited by 5
  32. 2022Link Prediction with Contextualized Self-Supervisioncited by 4
  33. 2023On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Poolingcited by 4
  34. 2024Improving the interpretability of GNN predictions through conformal-based graph sparsificationcited by 4
  35. 2021Edge but not Least: Cross-View Graph Poolingcited by 3
  36. 2022Supervised Contrastive Learning with Structure Inference for Graph Classificationcited by 3
  37. 2022A Class-Aware Representation Refinement Framework for Graph Classificationcited by 3
  38. 2022Position-Aware Subgraph Neural Networks with Data-Efficient Learningcited by 3

50 most cited of 75 All 75 (JSON)