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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
h5cb3713706640aac
Venue
VLDB
Year
2020
Pagerank
0.00012397734
Overall Rank
1,033 | 93.06%
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 21 of 21 citing papers.

Rank Citing Paper Year Venue Pagerank
1,134 SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks 2022 VLDB 0.00011893521
1,772 ByteGNN: Efficient Graph Neural Network Training at Large Scale 2022 VLDB 9.6792287e-05
2,279 NeutronStar: Distributed GNN Training with Hybrid Dependency Management 2022 SIGMOD 8.7062637e-05
2,596 Scalable and Efficient Full-Graph GNN Training for Large Graphs 2023 SIGMOD 8.2419456e-05
2,863 Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching 2022 VLDB 7.9301802e-05
4,622 Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning 2022 VLDB 6.4982876e-05
4,825 DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning 2024 VLDB 6.3923554e-05
4,898 Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective 2024 VLDB 6.365824e-05
4,993 DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks 2023 SIGMOD 6.32357e-05
7,405 Space-Efficient Random Walks on Streaming Graphs 2023 VLDB 5.5354654e-05
7,506 ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling 2023 SIGMOD 5.5070363e-05
7,708 Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods 2025 VLDB 5.4718627e-05
8,152 NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism 2025 VLDB 5.3896512e-05
8,623 D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks 2024 VLDB 5.3009314e-05
9,900 Scalable Graph Convolutional Network Training on Distributed-Memory Systems 2023 VLDB 5.1116429e-05
10,257 Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch 2025 VLDB 5.050482e-05
10,456 FastGNAS: Accelerating and Scaling Graph Neural Architecture Search on Multi-GPUs via Ring-Based Model Migration 2026 SIGMOD 4.9793485e-05
10,520 A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness 2026 SIGMOD 4.9793485e-05
10,939 BiLink: Bidirectional Meta-paths for Link Discovery in Billion-Scale Heterogeneous Graphs 2026 VLDB 4.9793485e-05
11,043 NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments 2026 VLDB 4.9793485e-05
11,564 FlowWalker: A Memory-efficient and High-performance GPU-based Dynamic Graph Random Walk Framework 2024 VLDB 4.9793485e-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
211 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024816965
394 One Trillion Edges: Graph Processing at Facebook-Scale 2015 VLDB 0.00019191286
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