research-article

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

Authors:

Lei ChenAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 17, Issue 5

Pages 1060 - 1072

https://doi.org/10.14778/3641204.3641215

Published: 01 January 2024 Publication History

Abstract

Dynamic graphs play a crucial role in various real-world applications, such as link prediction and node classification on social media and e-commerce platforms. Temporal Graph Neural Networks (T-GNNs) have emerged as a leading approach for handling dynamic graphs, using temporal message passing to compute temporal node embeddings. However, training existing T-GNNs on large-scale dynamic graphs is prohibitively expensive due to the ill-suited batching scheme and significant data access overhead. In this paper, we introduce ETC, a generic framework designed specifically for efficient T-GNN training at scale. ETC incorporates a novel data batching scheme that enables large training batches improving model computation efficiency, while preserving model effectiveness by restricting information loss in each training batch. To reduce data access overhead, ETC employs a three-step data access policy that leverages the data access pattern in T-GNN training, significantly reducing redundant data access volume. Additionally, ETC utilizes an inter-batch pipeline mechanism, decoupling data access from model computation and further reducing data access costs. Extensive experimental results demonstrate the effectiveness of ETC, showcasing its ability to achieve significant training speedups compared to state-of-the-art training frameworks for T-GNNs on real-world dynamic graphs with millions of interactions. ETC provides a training speedup ranging from 1.6X to 62.4X, highlighting its potential for efficient training on large-scale dynamic graphs.

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Cited By

Zeng YHou XWang XLi J(2025)Towards a Unified Temporal and Event Logic Paradigm for Multi-Hop Path Reasoning in Knowledge GraphsElectronics10.3390/electronics1403051614:3(516)Online publication date: 27-Jan-2025
https://doi.org/10.3390/electronics14030516
Wang YLi DSun JKun YJiang YZhang YCao J(2025)Combining macro and micro: feature-driven dynamic graph learning for social media popularity predictionWorld Wide Web10.1007/s11280-024-01323-x28:1Online publication date: 25-Jan-2025
https://doi.org/10.1007/s11280-024-01323-x
Shen YChen LFang JZhang XGao SYin H(2024)Efficient Training of Graph Neural Networks on Large GraphsProceedings of the VLDB Endowment10.14778/3685800.368584417:12(4237-4240)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.14778/3685800.3685844
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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 17, Issue 5

January 2024

233 pages

Editors:
Meihui Zhang
Beijing Institute of Technology
,
Cyrus Shahabi
University of Southern California

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VLDB Endowment

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Published: 01 January 2024

Published in PVLDB Volume 17, Issue 5

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Cited By

Zeng YHou XWang XLi J(2025)Towards a Unified Temporal and Event Logic Paradigm for Multi-Hop Path Reasoning in Knowledge GraphsElectronics10.3390/electronics1403051614:3(516)Online publication date: 27-Jan-2025
https://doi.org/10.3390/electronics14030516
Wang YLi DSun JKun YJiang YZhang YCao J(2025)Combining macro and micro: feature-driven dynamic graph learning for social media popularity predictionWorld Wide Web10.1007/s11280-024-01323-x28:1Online publication date: 25-Jan-2025
https://doi.org/10.1007/s11280-024-01323-x
Shen YChen LFang JZhang XGao SYin H(2024)Efficient Training of Graph Neural Networks on Large GraphsProceedings of the VLDB Endowment10.14778/3685800.368584417:12(4237-4240)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.14778/3685800.3685844
Li HDi SLi CChen LZhou X(2024)Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively DefenseProceedings of the VLDB Endowment10.14778/3659437.365945717:8(2050-2063)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659457
Li YShen YChen LYuan M(2024)A Caching-based Framework for Scalable Temporal Graph Neural Network TrainingACM Transactions on Database Systems10.1145/370589450:1(1-46)Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1145/3705894
Gao SLi YZhang XShen YShao YChen L(2024)SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data PlacementProceedings of the ACM on Management of Data10.1145/36549772:3(1-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654977
Wang RZhao WSun DMendis CAbdelzaher TSerra ESpezzano F(2024)Towards Efficient Temporal Graph Learning: Algorithms, Frameworks, and ToolsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679104(5530-5533)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679104
Zeb MUddin MAlarood AShafiq MHabibullah SAlsulami A(2024)Dynamic Neighborhood Selection for Context Aware Temporal Evolution Using Graph Neural NetworksCognitive Computation10.1007/s12559-024-10359-017:1Online publication date: 5-Dec-2024
https://doi.org/10.1007/s12559-024-10359-0

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