mlsanityData to 7 Oct 2026

Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey

arXiv 2203.10480 ↗First posted 20 Mar 2022cs.LGcited by 14

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Abstract (arXiv)

In recent years, the prevalent online services generate a sheer volume of user activity data. Service providers collect these data in order to perform client behavior analysis, and offer better and more customized services. Majority of these data can be modeled and stored as graph, such as the social graph in Facebook, user-video interaction graph in Youtube. These graphs need to evolve over time to capture the dynamics in the real world, leading to the invention of dynamic graphs. However, the temporal information embedded in the dynamic graphs brings new challenges in analyzing and deploying them. Events staleness, temporal information learning and explicit time dimension usage are some example challenges in dynamic graph learning. In order to offer a convenient reference to both the industry and academia, this survey presents the Three Stages Recurrent Temporal Learning Framework based on dynamic graph evolution theories, so as to interpret the learning of temporal information with a generalized framework. Under this framework, this survey categories and reviews different learnable encoder-decoder architectures for supervised dynamic graph learning. We believe that this survey could supply useful guidelines to researchers and engineers in finding suitable graph structures for their dynamic learning tasks.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 43 of its 101 references that are in the map. First posted on arXiv 20 Mar 2022.

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43 of 101 references.

References · 43

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Citing papers per year: 2022: 1; 2023: 5; 2024: 5; 2025: 3.

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  1. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  2. 2024A survey of dynamic graph neural networkscited by 21
  3. 2025Analysing the Robustness of Vision-Language-Models to Common Corruptionscited by 16
  4. 2024Continual Learning on Graphs: A Surveycited by 11
  5. 2023Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotioncited by 8
  6. 2024Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamicscited by 7
  7. 2023NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streamscited by 6
  8. 2022Influencer Detection with Dynamic Graph Neural Networkscited by 3
  9. 2023Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Surveycited by 3
  10. 2023INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networkscited by 1
  11. 2023Dynamic Link Prediction for New Nodes in Temporal Graph Networkscited by 1
  12. 2024Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsitycited by 1
2 more
  1. 2025Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphscited by 0
  2. 2025SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modelingcited by 0