mlsanityData to 14 Oct 2026

Variational Graph Convolutional Neural Networks

arXiv 2507.01699 ↗First posted 2 Jul 2025cs.LGcited by 0

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Abstract (arXiv)

Estimation of model uncertainty can help improve the explainability of Graph Convolutional Networks and the accuracy of the models at the same time. Uncertainty can also be used in critical applications to verify the results of the model by an expert or additional models. In this paper, we propose Variational Neural Network versions of spatial and spatio-temporal Graph Convolutional Networks. We estimate uncertainty in both outputs and layer-wise attentions of the models, which has the potential for improving model explainability. We showcase the benefits of these models in the social trading analysis and the skeleton-based human action recognition tasks on the Finnish board membership, NTU-60, NTU-120 and Kinetics datasets, where we show improvement in model accuracy in addition to estimated model uncertainties.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 46 references that are in the map. First posted on arXiv 2 Jul 2025.

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34 of 46 references.

References · 34

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2017The Kinetics Human Action Video Datasetcited by 1,909
  5. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  6. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,143
  7. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  8. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 686
  9. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  10. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  11. 2015Weight Uncertainty in Neural Networkscited by 554
  12. 2019NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understandingcited by 533
22 more
  1. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 280
  2. 2018Randomized Prior Functions for Deep Reinforcement Learningcited by 219
  3. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 153
  4. 2018DeepInf: Social Influence Prediction with Deep Learningcited by 128
  5. 2021Epistemic Neural Networkscited by 120
  6. 2018Bayesian graph convolutional neural networks for semi-supervised classificationcited by 89
  7. 2020Bayesian Graph Neural Networks with Adaptive Connection Samplingcited by 81
  8. 2018Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentationcited by 36
  9. 2019Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classificationcited by 36
  10. 2023GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentationcited by 31
  11. 2024Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognitioncited by 29
  12. 2022Bayesian Learning for Neural Networks: an algorithmic surveycited by 23
  13. 2024Uncertainty in Graph Neural Networks: A Surveycited by 23
  14. 2024MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Predictioncited by 18
  15. 2020Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Losscited by 10
  16. 2020Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognitioncited by 9
  17. 2020Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognitioncited by 8
  18. 2022Variational Neural Networkscited by 7
  19. 2021On the combination of graph data for assessing thin-file borrowers' creditworthinesscited by 4
  20. 2022Layer Ensemblescited by 3
  21. 2023Uncertainty-Aware AB3DMOT by Variational 3D Object Detectioncited by 3
  22. 2021Social Influence Prediction with Train and Test Time Augmentation for Graph Neural Networkscited by 1

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