mlsanityData to 13 Oct 2026

Variational Graph Normalized Auto-Encoders

arXiv 2108.08046 ↗First posted 18 Aug 2021cs.LG cs.AIcited by 29

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Abstract (arXiv)

Link prediction is one of the key problems for graph-structured data. With the advancement of graph neural networks, graph autoencoders (GAEs) and variational graph autoencoders (VGAEs) have been proposed to learn graph embeddings in an unsupervised way. It has been shown that these methods are effective for link prediction tasks. However, they do not work well in link predictions when a node whose degree is zero (i.g., isolated node) is involved. We have found that GAEs/VGAEs make embeddings of isolated nodes close to zero regardless of their content features. In this paper, we propose a novel Variational Graph Normalized AutoEncoder (VGNAE) that utilize L2-normalization to derive better embeddings for isolated nodes. We show that our VGNAEs outperform the existing state-of-the-art models for link prediction tasks. The code is available at https://github.com/SeongJinAhn/VGNAE.

Key facts. Cited by 29 papers in the map (data through 13 Oct 2026). Builds on 27 of its 32 references that are in the map. First posted on arXiv 18 Aug 2021.

Builds on

27 of 32 references.

References · 27

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15 more
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Citing papers per year: 2022: 2; 2023: 8; 2024: 13; 2025: 6.

29 papers.

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  1. 2023Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Datacited by 25
  2. 2024Reliable Node Similarity Matrix Guided Contrastive Graph Clusteringcited by 12
  3. 2024Conformal Load Prediction with Transductive Graph Autoencoderscited by 11
  4. 2024Accurate Link Prediction for Edge-Incomplete Graphs via PU Learningcited by 5
  5. 2025Enhanced Route Planning with Calibrated Uncertainty Setcited by 5
  6. 2023Independent Distribution Regularization for Private Graph Embeddingcited by 4
  7. 2024TEDDY: Trimming Edges with Degree-based Discrimination strategYcited by 4
  8. 2024Uncertainty Quantification on Graph Learning: A Surveycited by 4
  9. 2022Interpretable Node Representation with Attribute Decodingcited by 3
  10. 2023Variational Disentangled Graph Auto-Encoders for Link Predictioncited by 3
  11. 2022Graph Perceiver IO: A General Architecture for Graph Structured Datacited by 2
  12. 2023Evaluating Link Prediction Explanations for Graph Neural Networkscited by 2
17 more
  1. 2025Residual Reweighted Conformal Prediction for Graph Neural Networkscited by 2
  2. 2025Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networkscited by 2
  3. 2023NESS: Node Embeddings from Static SubGraphscited by 1
  4. 2023A parameterised model for link prediction using node centrality and similarity measure based on graph embeddingcited by 1
  5. 2024Introducing New Node Prediction in Graph Mining: Predicting All Links from Isolated Nodes with Graph Neural Networkscited by 1
  6. 2024Learning Network Representations with Disentangled Graph Auto-Encodercited by 1
  7. 2024PROXI: Challenging the GNNs for Link Predictioncited by 1
  8. 2025Norm Augmented Graph AutoEncoders for Link Predictioncited by 1
  9. 2025Tri-Learn Graph Fusion Network for Attributed Graph Clusteringcited by 1
  10. 2023CoCon: A Data Set on Combined Contextualized Research Artifact Usecited by 0
  11. 2023NP2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networkscited by 0
  12. 2024Performance Prediction of Hub-Based Swarmscited by 0
  13. 2024SCNode: Spatial and Contextual Coordinates for Graph Representation Learningcited by 0
  14. 2024Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Featurescited by 0
  15. 2024Can Self Supervision Rejuvenate Similarity-Based Link Prediction?cited by 0
  16. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0
  17. 2025Enhancing Contrastive Link Prediction With Edge Balancing Augmentationcited by 0