mlsanityData to 7 Oct 2026

Mask-GVAE: Blind Denoising Graphs via Partition

arXiv 2102.04228 ↗First posted 8 Feb 2021cs.LGcited by 9

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Abstract (arXiv)

We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs. We focus on recovering graph structures via deleting irrelevant edges and adding missing edges, which has many applications in real-world scenarios, for example, enhancing the quality of connections in a co-authorship network. Mask-GVAE makes use of the robustness in low eigenvectors of graph Laplacian against random noise and decomposes the input graph into several stable clusters. It then harnesses the huge computations by decoding probabilistic smoothed subgraphs in a variational manner. On a wide variety of benchmarks, Mask-GVAE outperforms competing approaches by a significant margin on PSNR and WL similarity.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 35 of its 68 references that are in the map. First posted on arXiv 8 Feb 2021.

Builds on

35 of 68 references.

References · 35

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
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  4. 2019Simplifying Graph Convolutional Networkscited by 1,470
  5. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
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  9. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
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23 more
  1. 2018Toward Convolutional Blind Denoising of Real Photographscited by 274
  2. 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 182
  3. 2018SpectralNet: Spectral Clustering using Deep Neural Networkscited by 94
  4. 2019Unsupervised speech representation learning using WaveNet autoencoderscited by 90
  5. 2018NeVAE: A Deep Generative Model for Molecular Graphscited by 66
  6. 2019Few-Shot Knowledge Graph Completioncited by 63
  7. 2019Semi-Supervised Graph Classification: A Hierarchical Graph Perspectivecited by 55
  8. 2016Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)cited by 44
  9. 2015Denoising Criterion for Variational Auto-Encoding Frameworkcited by 43
  10. 2020Dirichlet Graph Variational Autoencodercited by 40
  11. 2020Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothingcited by 29
  12. 2019Feature Graph Learning for 3D Point Cloud Denoisingcited by 28
  13. 2020Adversarial Attack on Community Detection by Hiding Individualscited by 20
  14. 2019GAP: Generalizable Approximate Graph Partitioning Frameworkcited by 18
  15. 2017Unperturbed: spectral analysis beyond Davis-Kahancited by 16
  16. 2018Network Enhancement: a general method to denoise weighted biological networkscited by 15
  17. 2018Deep Graph Laplacian Regularization for Robust Denoising of Real Imagescited by 13
  18. 2019Blind Denoising Autoencodercited by 13
  19. 2019A Degeneracy Framework for Scalable Graph Autoencoderscited by 12
  20. 2019Average Sensitivity of Graph Algorithmscited by 8
  21. 2020Average Sensitivity of Spectral Clusteringcited by 6
  22. 2015Distortive Effects of Initial-Based Name Disambiguation on Measurements of Large-Scale Coauthorship Networkscited by 4
  23. 2020FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decodingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 2; 2025: 0; 2026: 1.

9 papers.

  1. 2024All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretrainingcited by 84
  2. 2021Graph Sanitation with Application to Node Classificationcited by 20
  3. 2023A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignmentcited by 16
  4. 2023Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transportcited by 15
  5. 2023A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Datacited by 10
  6. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  7. 2022FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classificationcited by 7
  8. 2023Robust Graph Contrastive Learning with Information Restorationcited by 0
  9. 2026Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusioncited by 0