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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
12384
Venue
VLDB
Year
2020
Pagerank
0.00012433693
Overall Rank
1,048 | 92.82%
DOI
10.14778/3415478.3415539

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhang_vldb20,
        title = {{AGL: A Scalable System for Industrial-purpose Graph Machine Learning}},
        author = {Zhang, Dalong and Huang, Xin and Liu, Ziqi and Zhou, Jun and Hu, Zhiyang and Song, Xianzheng and Ge, Zhibang and Wang, Lin and Zhang, Zhiqiang and Qi, Yuan},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {12},
        pages = {3125--3137},
        doi = {10.14778/3415478.3415539},
        url = {https://doi.org/10.14778/3415478.3415539},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 20 of 20 citing papers.

Rank Citing Paper Year Venue Pagerank
1,132 SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks 2022 VLDB 0.00012041292
1,863 ByteGNN: Efficient Graph Neural Network Training at Large Scale 2022 VLDB 9.5950349e-05
2,640 Scalable and Efficient Full-Graph GNN Training for Large Graphs 2023 SIGMOD 8.3074486e-05
2,695 NeutronStar: Distributed GNN Training with Hybrid Dependency Management 2022 SIGMOD 8.2468134e-05
2,953 Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching 2022 VLDB 7.9237794e-05
4,549 Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning 2022 VLDB 6.6364104e-05
4,891 DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks 2023 SIGMOD 6.4581865e-05
5,316 Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective 2024 VLDB 6.2687017e-05
7,087 DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning 2024 VLDB 5.7069166e-05
7,255 Space-Efficient Random Walks on Streaming Graphs 2023 VLDB 5.662517e-05
7,374 ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling 2023 SIGMOD 5.6306367e-05
7,601 Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods 2025 VLDB 5.5866563e-05
8,454 D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks 2024 VLDB 5.4226e-05
9,546 NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism 2025 VLDB 5.2528121e-05
9,729 Scalable Graph Convolutional Network Training on Distributed-Memory Systems 2023 VLDB 5.2289669e-05
10,241 FastGNAS: Accelerating and Scaling Graph Neural Architecture Search on Multi-GPUs via Ring-Based Model Migration 2026 SIGMOD 5.093636e-05
10,309 A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness 2026 SIGMOD 5.093636e-05
10,596 NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments 2026 VLDB 5.093636e-05
10,811 Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch 2025 VLDB 5.093636e-05
11,228 FlowWalker: A Memory-efficient and High-performance GPU-based Dynamic Graph Random Walk Framework 2024 VLDB 5.093636e-05
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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.

Rank Cited Paper Year Venue Pagerank
223 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024182473
389 One Trillion Edges: Graph Processing at Facebook-Scale 2015 VLDB 0.00019386526
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4 7,601 Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods 2025 VLDB
5 10,521 Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling 2026 VLDB
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