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AGL: A Scalable System for Industrial-purpose Graph Machine Learning
Summary: AGL is a scalable, integrated system for industrial graph ML with both training and inference for GNNs. It builds K-hop information-complete subgraphs via MapReduce, enabling data-independent training on parameter servers and fast inference over massive graphs.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12196
- Venue
- VLDB
- Year
- 2020
- Pagerank
- 0.00012561848
- Overall Rank
- 1,329 | 90.76%
- DOI
-
10.14778/3415478.3415539
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 19 of 19 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 3,025 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6906935e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |
| 3,276 |
Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching |
2022 |
VLDB |
7.2879718e-05 |
| 5,007 |
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning |
2022 |
VLDB |
5.763689e-05 |
| 5,018 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7567672e-05 |
| 5,737 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3480667e-05 |
| 7,212 |
Space-Efficient Random Walks on Streaming Graphs |
2023 |
VLDB |
4.7989929e-05 |
| 7,289 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7747168e-05 |
| 7,566 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7089968e-05 |
| 7,607 |
Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods |
2025 |
VLDB |
4.6967024e-05 |
| 8,463 |
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks |
2024 |
VLDB |
4.5052127e-05 |
| 9,395 |
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism |
2025 |
VLDB |
4.3441378e-05 |
| 9,596 |
Scalable Graph Convolutional Network Training on Distributed-Memory Systems |
2023 |
VLDB |
4.319218e-05 |
| 10,011 |
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,298 |
NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments |
2026 |
VLDB |
4.1945683e-05 |
| 10,539 |
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch. |
2025 |
VLDB |
4.1945683e-05 |
| 11,017 |
FlowWalker: A Memory-efficient and High-performance GPU-based Dynamic Graph Random Walk Framework |
2024 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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