mlsanityData to 7 Oct 2026

Training Graph Neural Networks by Graphon Estimation

arXiv 2109.01918 ↗First posted 4 Sep 2021cs.LG cs.SIcited by 4

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Abstract (arXiv)

In this work, we propose to train a graph neural network via resampling from a graphon estimate obtained from the underlying network data. More specifically, the graphon or the link probability matrix of the underlying network is first obtained from which a new network will be resampled and used during the training process at each layer. Due to the uncertainty induced from the resampling, it helps mitigate the well-known issue of over-smoothing in a graph neural network (GNN) model. Our framework is general, computationally efficient, and conceptually simple. Another appealing feature of our method is that it requires minimal additional tuning during the training process. Extensive numerical results show that our approach is competitive with and in many cases outperform the other over-smoothing reducing GNN training methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 31 references that are in the map. First posted on arXiv 4 Sep 2021.

Builds on

22 of 31 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  7. 2016Variational Graph Auto-Encoderscited by 1,373
  8. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  9. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  10. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  11. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  12. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
10 more
  1. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  2. 2019Learning Discrete Structures for Graph Neural Networkscited by 228
  3. 2017CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filterscited by 203
  4. 2020Data Augmentation for Graph Neural Networkscited by 200
  5. 2018Bayesian graph convolutional neural networks for semi-supervised classificationcited by 88
  6. 2020Bayesian Graph Neural Networks with Adaptive Connection Samplingcited by 81
  7. 2015Estimating network edge probabilities by neighborhood smoothingcited by 28
  8. 2014A goodness-of-fit test for stochastic block modelscited by 20
  9. 2019Graph Neural Networks Exponentially Lose Expressive Power for Node Classificationcited by 16
  10. 2019Change-point detection in dynamic networks via graphon estimationcited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 145
  2. 2023Transferability of Graph Neural Networks using Graphon and Sampling Theoriescited by 2
  3. 2025SlideMamba: Entropy-Based Adaptive Fusion of GNN and Mamba for Enhanced Representation Learning in Digital Pathologycited by 1
  4. 2024Graph data augmentation with Gromow-Wasserstein Barycenterscited by 0