Back to papers
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
- 13117
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 4.8955332e-05
- Overall Rank
- 6,884 | 52.11%
- 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 |
0.0019005923 |
| 278 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029230623 |
| 683 |
Cerebro: A Data System for Optimized Deep Learning Model Selection |
2020 |
VLDB |
0.00018195476 |
| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 2,962 |
Kuzu* Graph Database Management System |
2023 |
CIDR |
7.8101752e-05 |
| 3,986 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
VLDB |
6.5611714e-05 |
| 4,557 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
6.087611e-05 |
| 5,017 |
TurboGraph++: A Scalable and Fast Graph Analytics System |
2018 |
SIGMOD |
5.7574792e-05 |
| 6,625 |
ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks |
2021 |
SIGMOD |
4.9889819e-05 |
| 7,656 |
Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets |
2022 |
SIGMOD |
4.6871575e-05 |
| 7,813 |
GraphScope: A One-Stop Large Graph Processing System |
2021 |
VLDB |
4.6441779e-05 |
| 8,864 |
Cerebro: A Layered Data Platform for Scalable Deep Learning |
2021 |
CIDR |
4.4326439e-05 |
| 9,222 |
Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning |
2021 |
VLDB |
4.3698672e-05 |
| 9,603 |
Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads |
2024 |
VLDB |
4.3177432e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 1,678 |
Navigating the Maze of Graph Analytics Frameworks using Massive Graph Datasets |
2014 |
SIGMOD |
0.00010933417 |
| 5,420 |
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization |
2022 |
VLDB |
5.5157743e-05 |
| 7,091 |
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs |
2023 |
SIGMOD |
4.8370645e-05 |
| 5,561 |
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses |
2024 |
VLDB |
5.4332062e-05 |
| 9,172 |
GraphGem: Optimized Scalable System for Graph Convolutional Networks |
2021 |
SIGMOD |
4.3845844e-05 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 5,737 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3480667e-05 |
| 1,329 |
AGL: A Scalable System for Industrial-purpose Graph Machine Learning |
2020 |
VLDB |
0.00012561848 |
| 6,942 |
Efficient Training of Graph Neural Networks on Large Graphs |
2024 |
VLDB |
4.8922884e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |