mlsanityData to 14 Oct 2026

Variational Graph Recurrent Neural Networks

arXiv 1908.09710 ↗First posted 26 Aug 2019cs.LG stat.MLcited by 87

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Abstract (arXiv)

Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we develop a novel hierarchical variational model that introduces additional latent random variables to jointly model the hidden states of a graph recurrent neural network (GRNN) to capture both topology and node attribute changes in dynamic graphs. We argue that the use of high-level latent random variables in this variational GRNN (VGRNN) can better capture potential variability observed in dynamic graphs as well as the uncertainty of node latent representation. With semi-implicit variational inference developed for this new VGRNN architecture (SI-VGRNN), we show that flexible non-Gaussian latent representations can further help dynamic graph analytic tasks. Our experiments with multiple real-world dynamic graph datasets demonstrate that SI-VGRNN and VGRNN consistently outperform the existing baseline and state-of-the-art methods by a significant margin in dynamic link prediction.

Key facts. Cited by 87 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Aug 2019.

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Citing papers per year: 2021: 16; 2022: 15; 2023: 21; 2024: 16; 2025: 10; 2026: 9.

87 papers.

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  1. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 193
  2. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 143
  3. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 138
  4. 2021Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Spacecited by 67
  5. 2022A Survey on Graph Representation Learning Methodscited by 66
  6. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
  7. 2021A Survey on Embedding Dynamic Graphscited by 60
  8. 2022Neighborhood-aware Scalable Temporal Network Representation Learningcited by 54
  9. 2021Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphscited by 50
  10. 2021HDMapGen: A Hierarchical Graph Generative Model of High Definition Mapscited by 40
  11. 2023LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?cited by 40
  12. 2023Continual Learning on Dynamic Graphs via Parameter Isolationcited by 36
38 more
  1. 2023Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoderscited by 32
  2. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 30
  3. 2023Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalizationcited by 26
  4. 2022A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learningcited by 24
  5. 2023Generalizing Graph ODE for Learning Complex System Dynamics across Environmentscited by 23
  6. 2024Uncertainty in Graph Neural Networks: A Surveycited by 23
  7. 2021Efficient and Interpretable Robot Manipulation with Graph Neural Networkscited by 22
  8. 2022Label-invariant Augmentation for Semi-Supervised Graph Classificationcited by 19
  9. 2024Spectral Invariant Learning for Dynamic Graphs under Distribution Shiftscited by 19
  10. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  11. 2023HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Predictioncited by 14
  12. 2024ReactZyme: A Benchmark for Enzyme-Reaction Predictioncited by 13
  13. 2023TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Supportcited by 12
  14. 2023DistTGL: Distributed Memory-Based Temporal Graph Neural Network Trainingcited by 12
  15. 2021On the Equivalence Between Temporal and Static Graph Representations for Observational Predictionscited by 10
  16. 2021Space-Time Graph Neural Networkscited by 10
  17. 2022Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysiscited by 10
  18. 2023BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networkscited by 9
  19. 2022Explaining Dynamic Graph Neural Networks via Relevance Back-propagationcited by 8
  20. 2024Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systemscited by 8
  21. 2021Revisiting Latent-Space Interpolation via a Quantitative Evaluation Frameworkcited by 7
  22. 2024Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-trainingcited by 7
  23. 2023Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Imagescited by 6
  24. 2024Supra-Laplacian Encoding for Transformer on Dynamic Graphscited by 6
  25. 2021Coarse-grained decomposition and fine-grained interaction for multi-hop question answeringcited by 5
  26. 2022Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphscited by 5
  27. 2023Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Predictioncited by 5
  28. 2024Bayesian Sheaf Neural Networkscited by 5
  29. 2023A Comprehensive Survey on Graph Summarization with Graph Neural Networkscited by 4
  30. 2024Temporal Link Prediction Using Graph Embedding Dynamicscited by 4
  31. 2024Uncertainty Quantification on Graph Learning: A Surveycited by 4
  32. 2021TD-GEN: Graph Generation With Tree Decompositioncited by 3
  33. 2021Bayesian Graph Contrastive Learningcited by 3
  34. 2022MoReL: Multi-omics Relational Learningcited by 3
  35. 2022Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysiscited by 3
  36. 2023A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identificationcited by 3
  37. 2024Input Snapshots Fusion for Scalable Discrete-Time Dynamic Graph Neural Networkscited by 3
  38. 2022Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Videocited by 2

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