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Representation Learning for Dynamic Graphs: A Survey

arXiv 1905.11485 ↗First posted 27 May 2019cs.LG stat.MLcited by 260

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Abstract (arXiv)

Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets and highlight directions for future research.

Key facts. Cited by 260 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 33; 2022: 35; 2023: 50; 2024: 69; 2025: 39; 2026: 34.

260 papers.

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  1. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  2. 2022Recurrent Vision Transformers for Object Detection with Event Camerascited by 158
  3. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  4. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 141
  5. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 137
  6. 2022Towards Better Evaluation for Dynamic Link Predictioncited by 120
  7. 2021Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphscited by 109
  8. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
  9. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  10. 2021Graph Representation Learning in Biomedicinecited by 79
  11. 2022A Survey on Graph Representation Learning Methodscited by 66
  12. 2021A Survey on Embedding Dynamic Graphscited by 60
38 more
  1. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  2. 2022TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphscited by 59
  3. 2022Provably expressive temporal graph networkscited by 56
  4. 2021Dynamic Network Embedding Surveycited by 44
  5. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  6. 2021Continual Learning of Knowledge Graph Embeddingscited by 35
  7. 2024TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphscited by 35
  8. 2021ChronoR: Rotation Based Temporal Knowledge Graph Embeddingcited by 34
  9. 2021Temporal Knowledge Graph Completion using Box Embeddingscited by 34
  10. 2023NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomicscited by 33
  11. 2022Graph Lifelong Learning: A Surveycited by 30
  12. 2023Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenancecited by 30
  13. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  14. 2021TempoQR: Temporal Question Reasoning over Knowledge Graphscited by 26
  15. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  16. 2023Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalizationcited by 26
  17. 2024Long Range Propagation on Continuous-Time Dynamic Graphscited by 26
  18. 2024DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphscited by 26
  19. 2022Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networkscited by 24
  20. 2022A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learningcited by 24
  21. 2024Graph Neural Networks for Financial Fraud Detection: A Reviewcited by 24
  22. 2024Graph Condensation: A Surveycited by 23
  23. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  24. 2022Graph Neural Networks Designed for Different Graph Types: A Surveycited by 21
  25. 2023Deep learning for dynamic graphs: models and benchmarkscited by 19
  26. 2022Proactive Robot Assistance via Spatio-Temporal Object Modelingcited by 18
  27. 2024Anomaly Detection in Dynamic Graphs: A Comprehensive Surveycited by 18
  28. 2021DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Datacited by 17
  29. 2023TIGER: Temporal Interaction Graph Embedding with Restartscited by 17
  30. 2022Instant Graph Neural Networks for Dynamic Graphscited by 16
  31. 2024State Space Models on Temporal Graphs: A First-Principles Studycited by 16
  32. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  33. 2023AutoSeqRec: Autoencoder for Efficient Sequential Recommendationcited by 15
  34. 2024Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformationcited by 15
  35. 2025TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamicscited by 15
  36. 2022Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Surveycited by 14
  37. 2023Decoupled Graph Neural Networks for Large Dynamic Graphscited by 14
  38. 2024SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learningcited by 14

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