mlsanityData to 7 Oct 2026

Dynamic Network Embedding Survey

arXiv 2103.15447 ↗First posted 29 Mar 2021cs.SI cs.AIcited by 44

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Abstract (arXiv)

Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent years. They learn node representations from a sequence of evolving graphs but not only the latest network, for preserving both structural and temporal information from the dynamic networks. Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper. Our survey inspects the data model, representation learning technique, evaluation and application of current related works and derives common patterns from them. Specifically, we present two basic data models, namely, discrete model and continuous model for dynamic networks. Correspondingly, we summarize two major categories of dynamic network embedding techniques, namely, structural-first and temporal-first that are adopted by most related works. Then we build a taxonomy that refines the category hierarchy by typical learning models. The popular experimental data sets and applications are also summarized. Lastly, we have a discussion of several distinct research topics in dynamic network embedding.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 31 of its 77 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

31 of 77 references.

References · 31

  1. 2017Graph Attention Networkscited by 8,854
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  7. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  8. 2019Heterogeneous Graph Attention Networkcited by 732
  9. 2015LINE: Large-scale Information Network Embeddingcited by 582
  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
19 more
  1. 2016Learning Convolutional Neural Networks for Graphscited by 314
  2. 2019Representation Learning for Dynamic Graphs: A Surveycited by 260
  3. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
  4. 2014word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodcited by 109
  5. 2016Motifs in Temporal Networkscited by 101
  6. 2018DynGEM: Deep Embedding Method for Dynamic Graphscited by 100
  7. 2018Streaming Graph Neural Networkscited by 91
  8. 2018dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learningcited by 88
  9. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  10. 2018Community Interaction and Conflict on the Webcited by 72
  11. 2019Temporal Network Embedding with Micro- and Macro-dynamicscited by 51
  12. 2017Attributed Network Embedding for Learning in a Dynamic Environmentcited by 39
  13. 2018Dynamic Graph Representation Learning via Self-Attention Networkscited by 39
  14. 2018dynnode2vec: Scalable Dynamic Network Embeddingcited by 32
  15. 2020Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNNcited by 23
  16. 2017TIMERS: Error-Bounded SVD Restart on Dynamic Networkscited by 19
  17. 2020A Survey on Dynamic Network Embeddingcited by 11
  18. 2020H-VGRAE: A Hierarchical Stochastic Spatial-Temporal Embedding Method for Robust Anomaly Detection in Dynamic Networkscited by 6
  19. 2020ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained levelcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 9; 2023: 13; 2024: 9; 2025: 6; 2026: 2.

44 papers.

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  1. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  2. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  3. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  4. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  5. 2022Graph Lifelong Learning: A Surveycited by 30
  6. 2024DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphscited by 26
  7. 2022DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graphcited by 21
  8. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  9. 2023MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligencecited by 20
  10. 2023Continual Graph Learning: A Surveycited by 17
  11. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  12. 2024Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformationcited by 15
32 more
  1. 2021Spectral embedding for dynamic networks with stability guaranteescited by 13
  2. 2023RDGSL: Dynamic Graph Representation Learning with Structure Learningcited by 13
  3. 2022Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphscited by 12
  4. 2024CLDG: Contrastive Learning on Dynamic Graphscited by 11
  5. 2022Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representationscited by 10
  6. 2022Model-Agnostic and Diverse Explanations for Streaming Rumour Graphscited by 9
  7. 2023TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformerscited by 9
  8. 2025A Survey of Link Prediction in Temporal Networkscited by 6
  9. 2025Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Modelscited by 6
  10. 2021ConTIG: Continuous Representation Learning on Temporal Interaction Graphscited by 5
  11. 2023Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networkscited by 5
  12. 2024Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identificationcited by 5
  13. 2022Graph similarity learning for change-point detection in dynamic networkscited by 4
  14. 2022Few-Shot Semantic Relation Prediction across Heterogeneous Graphscited by 4
  15. 2023iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservationcited by 4
  16. 2023Continuous-time Graph Representation with Sequential Survival Processcited by 4
  17. 2024Temporal Link Prediction Using Graph Embedding Dynamicscited by 4
  18. 2024Machine Learning on Dynamic Graphs: A Survey on Applicationscited by 4
  19. 2024From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learningcited by 3
  20. 2021Distributed Optimization of Graph Convolutional Network using Subgraph Variancecited by 2
  21. 2022Physics-Informed Graph Learningcited by 2
  22. 2023Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Networkcited by 1
  23. 2023ForeSeer: Product Aspect Forecasting Using Temporal Graph Embeddingcited by 1
  24. 2023Space-Invariant Projection in Streaming Network Embeddingcited by 0
  25. 2024Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Modelcited by 0
  26. 2024Non-Progressive Influence Maximization in Dynamic Social Networkscited by 0
  27. 2025Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architecturescited by 0
  28. 2025Unsupervised Attributed Dynamic Network Embedding with Stability Guaranteescited by 0
  29. 2025Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embeddingcited by 0
  30. 2025Trajectory Encoding Temporal Graph Networkscited by 0
  31. 2026Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphscited by 0
  32. 2026Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraphcited by 0