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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
- 6254
- Venue
- SIGMOD
- Year
- 2021
- Pagerank
- 0.00018825777
- Overall Rank
- 635 | 95.59%
- DOI
-
10.1145/3448016.3457564
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 21 of 21 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 1,388 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012249747 |
| 3,715 |
Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank |
2023 |
VLDB |
6.8176818e-05 |
| 5,169 |
HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training |
2022 |
SIGMOD |
5.642415e-05 |
| 5,356 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
5.5514335e-05 |
| 5,452 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
5.4972958e-05 |
| 5,485 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4817019e-05 |
| 5,721 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3538607e-05 |
| 6,479 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
5.0405101e-05 |
| 6,983 |
CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression |
2023 |
SIGMOD |
4.8682622e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8570865e-05 |
| 7,399 |
Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph |
2023 |
VLDB |
4.7357968e-05 |
| 7,750 |
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs |
2024 |
VLDB |
4.6565447e-05 |
| 8,507 |
Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense |
2024 |
VLDB |
4.4909322e-05 |
| 9,277 |
Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs |
2023 |
VLDB |
4.3610659e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,515 |
SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,642 |
PipeTGL: (Near) Zero Bubble Memory-based Temporal Graph Neural Network Training via Pipeline Optimization |
2025 |
VLDB |
4.1905499e-05 |
| 10,664 |
Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing |
2025 |
VLDB |
4.1905499e-05 |
| 10,681 |
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction |
2025 |
VLDB |
4.1905499e-05 |
| 10,889 |
Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph |
2025 |
VLDB |
4.1905499e-05 |
| 10,891 |
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours |
2025 |
VLDB |
4.1905499e-05 |
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 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 10,681 |
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction |
2025 |
VLDB |
4.1905499e-05 |
| 9,277 |
Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs |
2023 |
VLDB |
4.3610659e-05 |
| 10,891 |
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours |
2025 |
VLDB |
4.1905499e-05 |
| 2,165 |
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning |
2021 |
VLDB |
9.3925908e-05 |
| 6,115 |
GraphAn: Graph-based Subsequence Anomaly Detection |
2020 |
VLDB |
5.1995458e-05 |
| 8,736 |
Historical Embedding-Guided Efficient Large-Scale Federated Graph Learning |
2024 |
SIGMOD |
4.4520434e-05 |
| 4,578 |
Accelerating Dynamic Graph Analytics on GPUs |
2018 |
VLDB |
6.0651154e-05 |
| 7,157 |
GPU-Accelerated Graph Label Propagation for Real-Time Fraud Detection |
2021 |
SIGMOD |
4.8097601e-05 |
| 5,452 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
5.4972958e-05 |
| 10,939 |
NPA: Improving Large-scale Graph Neural Networks with Non-parametric Attention |
2024 |
SIGMOD |
4.1905499e-05 |