research-article

Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees

Authors:

Mingxuan YuanAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 1

Article No.: 52, Pages 1 - 27

https://doi.org/10.1145/3588737

Published: 30 May 2023 Publication History

Abstract

Representation learning over dynamic graphs is critical for many real-world applications such as social network services and recommender systems. Temporal graph neural networks (T-GNNs) are powerful representation learning methods and have achieved remarkable effectiveness on continuous-time dynamic graphs. However, T-GNNs still suffer from high time complexity, which increases linearly with the number of timestamps and grows exponentially with the model depth, causing them not scalable to large dynamic graphs. To address the limitations, we propose Orca, a novel framework that accelerates T-GNN training by non-trivially caching and reusing intermediate embeddings. We design an optimal cache replacement algorithm, named MRU, under a practical cache limit. MRU not only improves the efficiency of training T-GNNs by maximizing the number of cache hits but also reduces the approximation errors by avoiding keeping and reusing extremely stale embeddings. Meanwhile, we develop profound theoretical analyses of the approximation error introduced by our reuse schemes and offer rigorous convergence guarantees. Extensive experiments have validated that Orca can obtain two orders of magnitude speedup over the state-of-the-art baselines while achieving higher precision on large dynamic graphs.

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Cited By

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: 1-Aug-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
Gao SLi YShen YShao YChen L(2024)ETC: Efficient Training of Temporal Graph Neural Networks over Large-Scale Dynamic GraphsProceedings of the VLDB Endowment10.14778/3641204.364121517:5(1060-1072)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.14778/3641204.3641215
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Index Terms

Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees
1. Information systems
  1. Data management systems
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
      1. Dynamic graph algorithms

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 1

PACMMOD

May 2023

2807 pages

EISSN:2836-6573

DOI:10.1145/3603164

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Publication History

Published: 30 May 2023

Published in PACMMOD Volume 1, Issue 1

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Hong Kong ITC ITF
Hong Kong RGC AOE Project
Hong Kong RGC GRF Project
National Key Research and Development Program of China
Shanghai Municipal Science and Technology Major Project
National Science Foundation of China
Hong Kong RGC CRF Project
Guangdong Basic and Applied Basic Research Foundation
SJTU Global Strategic Partnership Fund
Hong Kong RGC Theme-based project
China NSFC
Microsoft Research Asia Collaborative Research Grant
HKUST-Webank joint research lab grant
HKUST Global Strategic Partnership Fund

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Cited By

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: 1-Aug-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
Gao SLi YShen YShao YChen L(2024)ETC: Efficient Training of Temporal Graph Neural Networks over Large-Scale Dynamic GraphsProceedings of the VLDB Endowment10.14778/3641204.364121517:5(1060-1072)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.14778/3641204.3641215
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
Wei ZTrummer I(2024)ROME: Robust Query Optimization via Parallel Multi-Plan ExecutionProceedings of the ACM on Management of Data10.1145/36549732:3(1-25)Online publication date: 30-May-2024
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Li YYang YCao JLiu STang HXu GBaeza-Yates RBonchi F(2024)Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing ApproachProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671848(1701-1712)Online publication date: 25-Aug-2024
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Xu YZhang WZhang YOrlowska MLin X(2024)TimeSGN: Scalable and Effective Temporal Graph Neural Network2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00255(3297-3310)Online publication date: 13-May-2024
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Liu ZWang CYang LLou YFeng HWu CZheng KSong Y(2024)Incorporating Dynamic Temperature Estimation into Contrastive Learning on Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00224(2889-2903)Online publication date: 13-May-2024
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