mlsanityData to 7 Oct 2026

LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation

arXiv 2302.01503 ↗First posted 3 Feb 2023cs.LGcited by 16

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Abstract (arXiv)

Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting scalability challenge due to the neighborhood explosion problem in large-scale graphs. In this work, we propose to capture long-distance dependency in graphs by shallower models instead of deeper models, which leads to a much more efficient model, LazyGNN, for graph representation learning. Moreover, we demonstrate that LazyGNN is compatible with existing scalable approaches (such as sampling methods) for further accelerations through the development of mini-batch LazyGNN. Comprehensive experiments demonstrate its superior prediction performance and scalability on large-scale benchmarks. The implementation of LazyGNN is available at https://github.com/RXPHD/Lazy_GNN.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 46 of its 60 references that are in the map. First posted on arXiv 3 Feb 2023.

Builds on

46 of 60 references.

References · 46

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34 more
  1. 2019Deep Equilibrium Modelscited by 603
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  3. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
  4. 2020Towards Deeper Graph Neural Networkscited by 296
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  6. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 199
  7. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  8. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  9. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  10. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
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  12. 2020A Unified View on Graph Neural Networks as Graph Signal Denoisingcited by 110
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 5; 2026: 1.

16 papers.

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  1. 2024Linear-Time Graph Neural Networks for Scalable Recommendationscited by 31
  2. 2023SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biasescited by 27
  3. 2023Probabilistically Rewired Message-Passing Neural Networkscited by 26
  4. 2024Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approachcited by 17
  5. 2025Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transformcited by 9
  6. 2023Efficient End-to-end Language Model Fine-tuning on Graphscited by 5
  7. 2024SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Modelscited by 5
  8. 2025Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Reportcited by 5
  9. 2025Unlearning Inversion Attacks for Graph Neural Networkscited by 4
  10. 2023MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energycited by 2
  11. 2024Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networkscited by 2
  12. 2025ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusioncited by 2
4 more
  1. 2024BloomGML: Graph Machine Learning through the Lens of Bilevel Optimizationcited by 1
  2. 2024HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networkscited by 0
  3. 2025GraphTorque: Torque-Driven Rewiring Graph Neural Networkcited by 0
  4. 2026ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learningcited by 0