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ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs

Summary: ETC: a framework for scalable T-GNN training with a novel batching scheme that enables large batches while controlling per-batch information loss. Uses a three-step data-access policy plus inter-batch pipelining to cut redundant I/O, yielding 1.6–62.4× speedups. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
h12e2e354f92b3660
Venue
VLDB
Year
2024
Pagerank
6.4276208e-05
Overall Rank
4,764 | 67.98%
DOI
10.14778/3641204.3641215

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{gao_vldb24,
        title = {{ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs}},
        author = {Gao, Shihong and Li, Yiming and Shen, Yanyan and Shao, Yingxia and Chen, Lei},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {5},
        pages = {1060--1072},
        doi = {10.14778/3641204.3641215},
        url = {https://doi.org/10.14778/3641204.3641215},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 13 of 13 citing papers.

Rank Citing Paper Year Venue Pagerank
6,166 Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving 2025 SIGMOD 5.8631131e-05
6,669 Efficient Training of Graph Neural Networks on Large Graphs 2024 VLDB 5.7147311e-05
6,826 SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement 2024 SIGMOD 5.6700316e-05
8,670 Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense 2024 VLDB 5.2917782e-05
9,232 PipeTGL: (Near) Zero Bubble Memory-based Temporal Graph Neural Network Training via Pipeline Optimization 2025 VLDB 5.2056825e-05
9,234 Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing 2025 VLDB 5.2056825e-05
10,535 SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine 2026 SIGMOD 4.9793485e-05
10,706 Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling 2026 VLDB 4.9793485e-05
10,779 PRISM: A Training System to Unlock the Potential of Temporal Graph Learning Through Staleness Avoidance 2026 VLDB 4.9793485e-05
11,066 Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation 2026 VLDB 4.9793485e-05
11,314 When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction 2025 VLDB 4.9793485e-05
11,361 Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation 2025 VLDB 4.9793485e-05
11,459 Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours 2025 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

Rank Cited Paper Year Venue Pagerank
211 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024816965
881 APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding 2021 SIGMOD 0.00013278822
1,134 SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks 2022 VLDB 0.00011893521
1,224 Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture 2021 VLDB 0.000114497
1,364 TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs 2022 VLDB 0.00010914152
1,772 ByteGNN: Efficient Graph Neural Network Training at Large Scale 2022 VLDB 9.6792287e-05
2,506 DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU 2023 SIGMOD 8.3774747e-05
3,017 Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank 2023 VLDB 7.7471344e-05
3,399 Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees 2023 SIGMOD 7.3349076e-05
4,945 HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training 2022 SIGMOD 6.3435891e-05
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