mlsanityData to 14 Oct 2026

Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks

arXiv 2405.13372 ↗First posted 22 May 2024cs.LGcited by 4

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Abstract (arXiv)

Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intricate associations in data, though scalability is a notable challenge due to memory limitations. In this study, we introduce a new adaptive sampling strategy specifically designed for hypergraphs, which tackles their unique complexities in an efficient manner. We also present a Random Hyperedge Augmentation (RHA) technique and an additional Multilayer Perceptron (MLP) module to improve the robustness and generalization capabilities of our approach. Thorough experiments with real-world datasets have proven the effectiveness of our method, markedly reducing computational and memory demands while maintaining performance levels akin to conventional HGNNs and other baseline models. This research paves the way for improving both the scalability and efficacy of HGNNs in extensive applications. We will also make our codebase publicly accessible.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 34 of its 69 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

34 of 69 references.

References · 34

  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. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
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  6. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
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  9. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 251
  10. 2019Hypergraph Convolution and Hypergraph Attentioncited by 228
  11. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  12. 2021UniGNN: a Unified Framework for Graph and Hypergraph Neural Networkscited by 130
22 more
  1. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 114
  2. 2020HNHN: Hypergraph Networks with Hyperedge Neuronscited by 109
  3. 2021Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendationcited by 101
  4. 2023Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimizationcited by 57
  5. 2022A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinkingcited by 56
  6. 2020HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphscited by 54
  7. 2019Random Walks on Hypergraphs with Edge-Dependent Vertex Weightscited by 45
  8. 2022MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initializationcited by 31
  9. 2018Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clusteringcited by 25
  10. 2020Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansioncited by 22
  11. 2022GFlowOut: Dropout with Generative Flow Networkscited by 22
  12. 2021Hyperbolic Hypergraphs for Sequential Recommendationcited by 21
  13. 2021Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poemscited by 18
  14. 2023From Hypergraph Energy Functions to Hypergraph Neural Networkscited by 18
  15. 2022Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphscited by 16
  16. 2021Click-Through Rate Prediction with Multi-Modal Hypergraphscited by 12
  17. 2022GFlowCausal: Generative Flow Networks for Causal Discoverycited by 11
  18. 2021Adaptive Neural Message Passing for Inductive Learning on Hypergraphscited by 10
  19. 2023Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graphcited by 10
  20. 2022A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddingscited by 9
  21. 2020Visual Analytics for Temporal Hypergraph Model Explorationcited by 6
  22. 2019HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalitiescited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 1.

4 papers.

  1. 2024Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Modelscited by 5
  2. 2024Automated Retinal Image Analysis and Medical Report Generation through Deep Learningcited by 1
  3. 2024Personalized Video Summarization using Text-Based Queries and Conditional Modelingcited by 1
  4. 2026MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understandingcited by 0