mlsanityData to 7 Oct 2026

Machine Learning on Dynamic Graphs: A Survey on Applications

arXiv 2401.08147 ↗First posted 16 Jan 2024cs.LG cs.SIcited by 4

Not on the map. Why?

Abstract (arXiv)

Dynamic graph learning has gained significant attention as it offers a powerful means to model intricate interactions among entities across various real-world and scientific domains. Notably, graphs serve as effective representations for diverse networks such as transportation, brain, social, and internet networks. Furthermore, the rapid advancements in machine learning have expanded the scope of dynamic graph applications beyond the aforementioned domains. In this paper, we present a review of lesser-explored applications of dynamic graph learning. This study revealed the potential of machine learning on dynamic graphs in addressing challenges across diverse domains, including those with limited levels of association with the field.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 50 of its 112 references that are in the map. First posted on arXiv 16 Jan 2024.

Builds on

50 of 112 references.

References · 50

  1. 2017Inductive Representation Learning on Large Graphscited by 6,284
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2019Simplifying Graph Convolutional Networkscited by 1,470
  4. 2020Simple and Deep Graph Convolutional Networkscited by 857
  5. 2017Deep Reinforcement Learning that Matterscited by 727
  6. 2016MOT16: A Benchmark for Multi-Object Trackingcited by 531
  7. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  8. 2021TrackFormer: Multi-Object Tracking with Transformerscited by 515
  9. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  10. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  11. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  12. 2020TransTrack: Multiple Object Tracking with Transformercited by 370
38 more
  1. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 361
  2. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  3. 2020MOT20: A benchmark for multi object tracking in crowded scenescited by 355
  4. 2017Deep Reinforcement Learning: An Overviewcited by 327
  5. 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
  6. 2019Representation Learning for Dynamic Graphs: A Surveycited by 260
  7. 2020Space-Time Correspondence as a Contrastive Random Walkcited by 239
  8. 2018An Introduction to Deep Reinforcement Learningcited by 201
  9. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  10. 2021PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learningcited by 181
  11. 2014Multiple Object Tracking: A Literature Reviewcited by 165
  12. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  13. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 158
  14. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  15. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 137
  16. 2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 131
  17. 2020A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sourcescited by 126
  18. 2022Transformer for Graphs: An Overview from Architecture Perspectivecited by 107
  19. 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
  20. 2021TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Trackingcited by 93
  21. 2018dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learningcited by 88
  22. 2021Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attentioncited by 86
  23. 2017Dynamic Graph Convolutional Networkscited by 83
  24. 2021A Survey on Embedding Dynamic Graphscited by 60
  25. 2020Understanding graph embedding methods and their applicationscited by 50
  26. 2021Dynamic Network Embedding Surveycited by 44
  27. 2019GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networkscited by 40
  28. 2020Random Walks: A Review of Algorithms and Applicationscited by 38
  29. 2021Machine learning and deep learningcited by 26
  30. 2022Temporal Link Prediction: A Unified Framework, Taxonomy, and Reviewcited by 20
  31. 2023MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detectioncited by 20
  32. 2021Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influencescited by 18
  33. 2019Random walks on hypergraphscited by 16
  34. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  35. 2017Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic datacited by 12
  36. 2020Time-Varying Graph Learning with Constraints on Graph Temporal Variationcited by 11
  37. 2019Dynamic Joint Variational Graph Autoencoderscited by 8
  38. 2021Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networkscited by 4

Built on by

Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Future Link Prediction Without Memory or Aggregationcited by 5
  2. 2025The Robustness of Structural Features in Species Interaction Networkscited by 2
  3. 2025A Review and Analysis of a Parallel Approach for Decision Tree Learning from Large Data Streamscited by 0
  4. 2025A Low-Cost Machine Learning Approach for Timber Diameter Estimationcited by 0