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SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement

Summary: SIMPLE targets the CPU-GPU data-loading bottleneck in large-scale temporal GNN training. Key idea: dynamic data placement with a small GPU buffer plus pipeline optimizations, cutting loading cost up to 96.8% and speeding training 1.8x–3.8x over TGL. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
6999
Venue
SIGMOD
Year
2024
Pagerank
5.7895038e-05
Overall Rank
6,730 | 53.83%
DOI
10.1145/3654977

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gao_sigmod24,
        title = {{SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement}},
        author = {Gao, Shihong and Li, Yiming and Zhang, Xin and Shen, Yanyan and Shao, Yingxia and Chen, Lei},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654977},
        url = {https://dl.acm.org/doi/10.1145/3654977},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank Citing Paper Year Venue Pagerank
6,630 Efficient Training of Graph Neural Networks on Large Graphs 2024 VLDB 5.8192133e-05
6,900 Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving 2025 SIGMOD 5.7430032e-05
9,880 MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training 2025 SIGMOD 5.2040783e-05
10,330 SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine 2026 SIGMOD 5.093636e-05
10,521 Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling 2026 VLDB 5.093636e-05
10,620 Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation 2026 VLDB 5.093636e-05
10,907 Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing 2025 VLDB 5.093636e-05
10,922 When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction 2025 VLDB 5.093636e-05
10,975 Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

Rank Cited Paper Year Venue Pagerank
223 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024182473
231 The LRU-K Page Replacement Algorithm For Database Disk Buffering 1993 SIGMOD 0.00023846898
920 APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding 2021 SIGMOD 0.00013209734
1,132 SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks 2022 VLDB 0.00012041292
1,234 Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture 2021 VLDB 0.00011549432
1,409 TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs 2022 VLDB 0.00010854808
1,863 ByteGNN: Efficient Graph Neural Network Training at Large Scale 2022 VLDB 9.5950349e-05
2,668 DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU 2023 SIGMOD 8.2750247e-05
2,888 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.9941489e-05
3,210 Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank 2023 VLDB 7.6352864e-05
3,634 Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees 2023 SIGMOD 7.2358691e-05
5,205 ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs 2024 VLDB 6.318626e-05
7,087 DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning 2024 VLDB 5.7069166e-05
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