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Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines
Summary: Lotan reframes full‑batch GNN training as query‑plan dataflows and decouples graph and DL scaling, introducing GNN‑centric partitioning and the first model‑batching scheme. Prototype on GraphX+PyTorch shows much greater scalability than custom GNN systems while often matching or only slightly trailing them on time‑to‑accuracy.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13118
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 4.8908367e-05
- Overall Rank
- 6,885 | 52.15%
- DOI
-
10.14778/3611479.3611483
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 4 |
Pregel: A System for Large-Scale Graph Processing |
2010 |
SIGMOD |
0.0019040811 |
| 271 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029565193 |
| 684 |
Cerebro: A Data System for Optimized Deep Learning Model Selection |
2020 |
VLDB |
0.00018152321 |
| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 2,962 |
Kuzu* Graph Database Management System |
2023 |
CIDR |
7.8069285e-05 |
| 3,988 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
VLDB |
6.5548465e-05 |
| 4,601 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
6.05274e-05 |
| 5,014 |
TurboGraph++: A Scalable and Fast Graph Analytics System |
2018 |
SIGMOD |
5.7519428e-05 |
| 6,624 |
ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks |
2021 |
SIGMOD |
4.9841936e-05 |
| 7,656 |
Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets |
2022 |
SIGMOD |
4.6826896e-05 |
| 7,819 |
GraphScope: A One-Stop Large Graph Processing System |
2021 |
VLDB |
4.6397234e-05 |
| 8,864 |
Cerebro: A Layered Data Platform for Scalable Deep Learning |
2021 |
CIDR |
4.4283952e-05 |
| 9,225 |
Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning |
2021 |
VLDB |
4.3656789e-05 |
| 9,603 |
Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads |
2024 |
VLDB |
4.3136057e-05 |
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Paper |
Year |
Venue |
Pagerank |
| 1,679 |
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| 5,431 |
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VLDB |
5.5104776e-05 |
| 7,087 |
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs |
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SIGMOD |
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| 5,570 |
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VLDB |
5.4280174e-05 |
| 9,176 |
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SIGMOD |
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| 2,399 |
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8.8869693e-05 |
| 5,746 |
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VLDB |
5.3429324e-05 |
| 1,332 |
AGL: A Scalable System for Industrial-purpose Graph Machine Learning |
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VLDB |
0.00012549751 |
| 6,944 |
Efficient Training of Graph Neural Networks on Large Graphs |
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VLDB |
4.8875946e-05 |
| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5869574e-05 |