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Hypergraph Neural Networks

arXiv 1809.09401 ↗First posted 25 Sep 2018cs.LG stat.MLcited by 482

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Abstract (arXiv)

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.

Key facts. Cited by 482 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2018.

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Citing papers per year: 2021: 56; 2022: 65; 2023: 73; 2024: 98; 2025: 91; 2026: 99.

482 papers.

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  1. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 461
  2. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
  3. 2022Graph neural networks for materials science and chemistrycited by 181
  4. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  5. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  6. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  7. 2021UniGNN: a Unified Framework for Graph and Hypergraph Neural Networkscited by 127
  8. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 124
  9. 2021Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphscited by 109
  10. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
  11. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 81
  12. 20243D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunitiescited by 77
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  1. 2021Graph-MLP: Node Classification without Message Passing in Graphcited by 73
  2. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  3. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  4. 2021Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identificationcited by 63
  5. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
  6. 2021A Survey on Embedding Dynamic Graphscited by 60
  7. 2025YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perceptioncited by 57
  8. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  9. 2022Multimodal learning with graphscited by 54
  10. 2021Complex Knowledge Base Question Answering: A Surveycited by 52
  11. 2021Affective Image Content Analysis: Two Decades Review and New Perspectivescited by 47
  12. 2022SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learningcited by 47
  13. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 45
  14. 2023Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networkscited by 43
  15. 2022Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendationcited by 42
  16. 2023Towards Self-Interpretable Graph-Level Anomaly Detectioncited by 41
  17. 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
  18. 2024Hypformer: Exploring Efficient Transformer Fully in Hyperbolic Spacecited by 39
  19. 2021Edge Representation Learning with Hypergraphscited by 37
  20. 2022Hypergraph Transformer for Skeleton-based Action Recognitioncited by 36
  21. 2022Cell Attention Networkscited by 34
  22. 2023Sheaf Hypergraph Networkscited by 33
  23. 2021Double-Scale Self-Supervised Hypergraph Learning for Group Recommendationcited by 32
  24. 2022Learning Causal Effects on Hypergraphscited by 31
  25. 2024Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computationcited by 29
  26. 2021ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completioncited by 28
  27. 2022Augmentations in Hypergraph Contrastive Learning: Fabricated and Generativecited by 28
  28. 2023Class-Imbalanced Learning on Graphs: A Surveycited by 24
  29. 2023Conditional Graph Information Bottleneck for Molecular Relational Learningcited by 24
  30. 2024TopoBench: A Framework for Benchmarking Topological Deep Learningcited by 24
  31. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  32. 2023Data-centric Graph Learning: A Surveycited by 22
  33. 2023Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challengescited by 22
  34. 2024TopoX: A Suite of Python Packages for Machine Learning on Topological Domainscited by 22
  35. 2024A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guidecited by 22
  36. 2024E(n) Equivariant Topological Neural Networkscited by 22
  37. 2024Learnable Expansion of Graph Operators for Multi-Modal Feature Fusioncited by 22
  38. 2022Graph Neural Networks Designed for Different Graph Types: A Surveycited by 21

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