mlsanityData to 14 Oct 2026

Hypergraph Convolution and Hypergraph Attention

arXiv 1901.08150 ↗First posted 23 Jan 2019cs.LG cs.CV stat.MLcited by 228

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Abstract (arXiv)

Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation. To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention. Whilst hypergraph convolution defines the basic formulation of performing convolution on a hypergraph, hypergraph attention further enhances the capacity of representation learning by leveraging an attention module. With the two operators, a graph neural network is readily extended to a more flexible model and applied to diverse applications where non-pairwise relationships are observed. Extensive experimental results with semi-supervised node classification demonstrate the effectiveness of hypergraph convolution and hypergraph attention.

Key facts. Cited by 228 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Jan 2019.

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Citing papers per year: 2021: 24; 2022: 27; 2023: 37; 2024: 53; 2025: 45; 2026: 42.

228 papers.

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  1. 2021UniGNN: a Unified Framework for Graph and Hypergraph Neural Networkscited by 130
  2. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 127
  3. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 82
  4. 2022A Survey on Graph Representation Learning Methodscited by 66
  5. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 66
  6. 2021Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identificationcited by 63
  7. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 47
  8. 2023Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networkscited by 43
  9. 2023Towards Self-Interpretable Graph-Level Anomaly Detectioncited by 41
  10. 2022Hypergraph Transformer for Skeleton-based Action Recognitioncited by 36
  11. 2023Sheaf Hypergraph Networkscited by 34
  12. 2022Learning Causal Effects on Hypergraphscited by 31
38 more
  1. 2022Augmentations in Hypergraph Contrastive Learning: Fabricated and Generativecited by 29
  2. 2024A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guidecited by 23
  3. 2024Hyper-3DG: Text-to-3D Gaussian Generation via Hypergraphcited by 22
  4. 2022Graph Neural Networks Designed for Different Graph Types: A Surveycited by 21
  5. 2022I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphscited by 21
  6. 2024Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecastingcited by 21
  7. 2022Preventing Over-Smoothing for Hypergraph Neural Networkscited by 20
  8. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  9. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 19
  10. 2024HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipationcited by 19
  11. 2023From Hypergraph Energy Functions to Hypergraph Neural Networkscited by 18
  12. 2023Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Designcited by 15
  13. 2022Equivariant Hypergraph Neural Networkscited by 14
  14. 2023Classification of Edge-dependent Labels of Nodes in Hypergraphscited by 14
  15. 2024Nonverbal Interaction Detectioncited by 14
  16. 2021Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Taskscited by 13
  17. 2021Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networkscited by 13
  18. 2022HEAT: Hyperedge Attention Networkscited by 13
  19. 2022Message Passing Neural Networks for Hypergraphscited by 13
  20. 2024SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognitioncited by 13
  21. 2021Learning Multi-Attention Context Graph for Group-Based Re-Identificationcited by 12
  22. 2024MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecastingcited by 12
  23. 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Frameworkcited by 12
  24. 2023Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networkscited by 11
  25. 2024Attending to Topological Spaces: The Cellular Transformercited by 11
  26. 2024Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connectionscited by 11
  27. 2025HyperG: Hypergraph-Enhanced LLMs for Structured Knowledgecited by 11
  28. 2021Adaptive Neural Message Passing for Inductive Learning on Hypergraphscited by 10
  29. 2023HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Levelcited by 10
  30. 2023Hypergraph Isomorphism Computationcited by 10
  31. 2023Learning High-Order Relationships of Brain Regionscited by 10
  32. 2024Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLMcited by 10
  33. 2022A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddingscited by 9
  34. 2025FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Predictioncited by 9
  35. 2025HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcarecited by 9
  36. 2021Principled Hyperedge Prediction with Structural Spectral Features and Neural Networkscited by 7
  37. 2022Deep Hypergraph Structure Learningcited by 7
  38. 2023Hypergraph Transformer for Semi-Supervised Classificationcited by 7

50 most cited of 228 All 228 (JSON)