mlsanityData to 7 Oct 2026

APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding

arXiv 2011.11545 ↗First posted 23 Nov 2020cs.AI cs.DB cs.SIcited by 78

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Abstract (arXiv)

Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference. This problem dramatically limits the potential of applying graph algorithms in certain areas, such as financial fraud detection. Therefore, we propose Asynchronous Propagation Attention Network, an asynchronous continuous time dynamic graph algorithm for real-time temporal graph embedding. Traditional graph models usually execute two serial operations: first graph computation and then model inference. We decouple model inference and graph computation step so that the heavy graph query operations will not damage the speed of model inference. Extensive experiments demonstrate that the proposed method can achieve competitive performance and 8.7 times inference speed improvement in the meantime.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 13; 2023: 19; 2024: 20; 2025: 13; 2026: 9.

78 papers.

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  1. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  2. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 141
  3. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  4. 2022TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphscited by 59
  5. 2022Provably expressive temporal graph networkscited by 56
  6. 2022Neighborhood-aware Scalable Temporal Network Representation Learningcited by 54
  7. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  8. 2024A survey of dynamic graph neural networkscited by 21
  9. 2024Efficient Neural Common Neighbor for Temporal Graph Link Predictioncited by 18
  10. 2023TIGER: Temporal Interaction Graph Embedding with Restartscited by 17
  11. 2022BRIGHT -- Graph Neural Networks in Real-Time Fraud Detectioncited by 15
  12. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
38 more
  1. 2025TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamicscited by 15
  2. 2022Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Surveycited by 14
  3. 2023Decoupled Graph Neural Networks for Large Dynamic Graphscited by 14
  4. 2024SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learningcited by 14
  5. 2024DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Modelscited by 14
  6. 2022Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphscited by 13
  7. 2023Towards Open Temporal Graph Neural Networkscited by 13
  8. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 13
  9. 2022Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGAcited by 12
  10. 2023DistTGL: Distributed Memory-Based Temporal Graph Neural Network Trainingcited by 12
  11. 2024MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipelinecited by 12
  12. 2023HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformerscited by 10
  13. 2021Time-Aware Neighbor Sampling for Temporal Graph Networkscited by 9
  14. 2023Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphscited by 9
  15. 2024Training MLPs on Graphs without Supervisioncited by 8
  16. 2022Representation Learning in Continuous-Time Dynamic Signed Networkscited by 6
  17. 2023NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streamscited by 6
  18. 2024Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encodingcited by 6
  19. 2024THeGCN: Temporal Heterophilic Graph Convolutional Networkcited by 6
  20. 2025A Survey of Link Prediction in Temporal Networkscited by 6
  21. 2025Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Modelscited by 6
  22. 2022Modelling graph dynamics in fraud detection with "Attention"cited by 5
  23. 2022DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Predictioncited by 5
  24. 2023GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Methodcited by 5
  25. 2024Temporal-Aware Evaluation and Learning for Temporal Graph Neural Networkscited by 5
  26. 2025TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformercited by 5
  27. 2022Continuous Temporal Graph Networks for Event-Based Graph Datacited by 4
  28. 2023iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservationcited by 4
  29. 2024Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networkscited by 4
  30. 2024Retrofitting Temporal Graph Neural Networks with Transformercited by 4
  31. 2025Bridging Theory and Practice in Link Representation with Graph Neural Networkscited by 4
  32. 2024Input Snapshots Fusion for Scalable Discrete-Time Dynamic Graph Neural Networkscited by 3
  33. 2025UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphscited by 3
  34. 2021Temporal Graph Network Embedding with Causal Anonymous Walks Representationscited by 2
  35. 2021Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecturecited by 2
  36. 2022TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagationcited by 2
  37. 2024Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Samplingcited by 2
  38. 2024Link-aware link prediction over temporal graph by pattern recognitioncited by 2

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