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TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
Summary: TGL is a general offline framework for training Temporal Graph Neural Networks on billion-scale graphs, configurable by files, with a temporal sampler, memory, and a message-passing engine. Key ideas: Temporal-CSR and random chunk scheduling enable large-scale multi-GPU/CPU training, yielding 13x single-GPU speedups and 173x CPU speedups on >1B temporal edges.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12663
- Venue
- VLDB
- Year
- 2022
- Pagerank
- 0.0001097193
- Overall Rank
- 1,408 | 90.22%
- DOI
-
10.14778/3529337.3529342
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 20 of 20 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,172 |
Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank |
2023 |
VLDB |
7.7409063e-05 |
| 4,800 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
6.5686201e-05 |
| 4,826 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
6.558209e-05 |
| 4,939 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
6.50013e-05 |
| 5,133 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
6.416487e-05 |
| 6,113 |
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning |
2023 |
VLDB |
6.036121e-05 |
| 6,168 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
6.028641e-05 |
| 6,627 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
5.8788666e-05 |
| 7,464 |
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs |
2024 |
VLDB |
5.6733565e-05 |
| 8,328 |
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks |
2024 |
VLDB |
5.5065836e-05 |
| 9,216 |
Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs |
2023 |
VLDB |
5.3642848e-05 |
| 9,584 |
Scalable Graph Convolutional Network Training on Distributed-Memory Systems |
2023 |
VLDB |
5.3099516e-05 |
| 9,778 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
5.2743647e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,334 |
Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation |
2026 |
VLDB |
5.1725247e-05 |
| 10,515 |
SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training |
2025 |
SIGMOD |
5.1725247e-05 |
| 10,642 |
PipeTGL: (Near) Zero Bubble Memory-based Temporal Graph Neural Network Training via Pipeline Optimization |
2025 |
VLDB |
5.1725247e-05 |
| 10,664 |
Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing |
2025 |
VLDB |
5.1725247e-05 |
| 10,681 |
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction |
2025 |
VLDB |
5.1725247e-05 |
| 10,891 |
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours |
2025 |
VLDB |
5.1725247e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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