mlsanityData to 7 Oct 2026

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

arXiv 2302.01018 ↗First posted 2 Feb 2023cs.LG cs.AIcited by 60

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Abstract (arXiv)

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current state-of-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). Builds on 77 of its 142 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

77 of 142 references.

References · 77

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
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  7. 2019Simplifying Graph Convolutional Networkscited by 1,470
  8. 2016Variational Graph Auto-Encoderscited by 1,373
  9. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  10. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  11. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  12. 2020Learning Mesh-Based Simulation with Graph Networkscited by 840
38 more
  1. 2018Link Prediction Based on Graph Neural Networkscited by 829
  2. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  3. 2019Graph Neural Networks for Social Recommendationcited by 711
  4. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  5. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  6. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  7. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 493
  8. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  9. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  10. 2020Inductive Representation Learning on Temporal Graphscited by 454
  11. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  12. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  13. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  14. 2018Deep Learning on Graphs: A Surveycited by 393
  15. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 362
  16. 2019Representation Learning for Dynamic Graphs: A Surveycited by 260
  17. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  18. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  19. 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 208
  20. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 188
  21. 2022Forecasting Global Weather with Graph Neural Networkscited by 165
  22. 2020Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systemscited by 160
  23. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
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  25. 2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 131
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  29. 2018Large-Scale Learnable Graph Convolutional Networkscited by 113
  30. 2020A Survey on The Expressive Power of Graph Neural Networkscited by 111
  31. 2018DynGEM: Deep Embedding Method for Dynamic Graphscited by 100
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  34. 2019Variational Graph Recurrent Neural Networkscited by 86
  35. 2020APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embeddingcited by 78
  36. 2021What are higher-order networks?cited by 66
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  38. 2022Global Explainability of GNNs via Logic Combination of Learned Conceptscited by 64

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 11; 2024: 19; 2025: 17; 2026: 13.

60 papers.

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  1. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  2. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  3. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  4. 2025Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learningcited by 18
  5. 2024State Space Models on Temporal Graphs: A First-Principles Studycited by 16
  6. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  7. 2024On The Temporal Domain of Differential Equation Inspired Graph Neural Networkscited by 9
  8. 2024UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphscited by 9
  9. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  10. 2023CAT-Walk: Inductive Hypergraph Learning via Set Walkscited by 6
  11. 2023CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networkscited by 6
  12. 2023Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resourcescited by 6
38 more
  1. 2025Over-squashing in Spatiotemporal Graph Neural Networkscited by 6
  2. 2023Temporal Graph Representation Learning with Adaptive Augmentation Contrastivecited by 5
  3. 2024Nonlinear Sheaf Diffusion in Graph Neural Networkscited by 4
  4. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  5. 2024Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphscited by 4
  6. 2025A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunitiescited by 4
  7. 2026Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspectivecited by 4
  8. 2023Generalization Limits of Graph Neural Networks in Identity Effects Learningcited by 3
  9. 2024A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformerscited by 3
  10. 2025Are Large Language Models Good Temporal Graph Learners?cited by 3
  11. 2024CoDy: Counterfactual Explainers for Dynamic Graphscited by 2
  12. 2024MiNT: Multi-Network Training for Transfer Learning on Temporal Graphscited by 2
  13. 2024Learning production functions for supply chains with graph neural networkscited by 2
  14. 2024Interpreting Temporal Graph Neural Networks with Koopman Theorycited by 2
  15. 2025Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learningcited by 2
  16. 2025The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logicscited by 2
  17. 2025Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphscited by 2
  18. 2025Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networkscited by 2
  19. 2026Temporal Graph Pattern Machinecited by 2
  20. 2026Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphscited by 2
  21. 2023DURENDAL: Graph deep learning framework for temporal heterogeneous networkscited by 1
  22. 2024Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithmscited by 1
  23. 2024Inference of Sequential Patterns for Neural Message Passing in Temporal Graphscited by 1
  24. 2023Analysis of different temporal graph neural network configurations on dynamic graphscited by 0
  25. 2023LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learningcited by 0
  26. 2024Online Algorithm for Node Feature Forecasting in Temporal Graphscited by 0
  27. 2024Analysing the Behaviour of Tree-Based Neural Networks in Regression Taskscited by 0
  28. 2024Temporal receptive field in dynamic graph learning: A comprehensive analysiscited by 0
  29. 2024Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Predictioncited by 0
  30. 2025STX-Search: Explanation Search for Continuous Dynamic Spatio-Temporal Modelscited by 0
  31. 2025Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphscited by 0
  32. 2025iN2V: Bringing Transductive Node Embeddings to Inductive Graphscited by 0
  33. 2025Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIncited by 0
  34. 2025Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoningcited by 0
  35. 2025Temporal social network modeling of mobile connectivity data with graph neural networkscited by 0
  36. 2025Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approachcited by 0
  37. 2025Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Predictioncited by 0
  38. 2025Debugging Tabular Log as Dynamic Graphscited by 0

50 most cited of 60 All 60 (JSON)