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

Summary: APAN enables real-time temporal graph embedding with asynchronous propagation attention, decoupling inference from k-hop querying. This yields millisecond-level inference in dense networks with competitive accuracy; code released. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hfc2feb181c8c2b54
Venue
SIGMOD
Year
2021
Pagerank
0.00013272536
Overall Rank
881 | 94.09%
DOI
10.1145/3448016.3457564

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod21,
        title = {{APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding}},
        author = {Wang, Xuhong and Lyu, Ding and Li, Mengjian and Xia, Yang and Yang, Qi and Wang, Xinwen and Wang, Xinguang and Cui, Ping and Yang, Yupu and Sun, Bowen and Guo, Zhenyu},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457564},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457564},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 22 of 22 citing papers.

Rank Citing Paper Year Venue Pagerank
1,365 TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs 2022 VLDB 0.00010908985
3,018 Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank 2023 VLDB 7.743467e-05
4,504 NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams 2024 VLDB 6.5709602e-05
4,767 ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs 2024 VLDB 6.4245781e-05
4,949 HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training 2022 SIGMOD 6.3405861e-05
5,055 Decoupled Graph Neural Networks for Large Dynamic Graphs 2023 VLDB 6.293158e-05
5,315 DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training 2024 VLDB 6.1828165e-05
6,110 GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs 2024 VLDB 5.8817721e-05
6,328 EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs 2023 SIGMOD 5.8110274e-05
6,529 CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression 2023 SIGMOD 5.7532491e-05
6,831 SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement 2024 SIGMOD 5.6673474e-05
7,555 Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph 2023 VLDB 5.4955102e-05
8,678 Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense 2024 VLDB 5.2892731e-05
9,242 PipeTGL: (Near) Zero Bubble Memory-based Temporal Graph Neural Network Training via Pipeline Optimization 2025 VLDB 5.2032182e-05
9,244 Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing 2025 VLDB 5.2032182e-05
9,537 Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs 2023 VLDB 5.1631953e-05
10,546 SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine 2026 SIGMOD 4.9769913e-05
10,789 PRISM: A Training System to Unlock the Potential of Temporal Graph Learning Through Staleness Avoidance 2026 VLDB 4.9769913e-05
11,207 SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training 2025 SIGMOD 4.9769913e-05
11,322 When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction 2025 VLDB 4.9769913e-05
11,464 Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph 2025 VLDB 4.9769913e-05
11,465 Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours 2025 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
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