The Story of GraphLab - From Scaling Machine Learning to Shaping Graph Systems Research (VLDB 2023 Test-of-time Award Talk)
Summary: Test-of-time retrospective on GraphLab’s vertex-/edge-centric abstractions and high-performance asynchronous, out-of-core execution for scalable ML and graph processing. Traces its influence on graph systems, databases, statistical inference, and industry via Turi. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Joseph E. Gonzalez (University of California Berkeley)
- 2. Yucheng Low (XetHub Inc.)
BibTeX Citation
@article{gonzalez_vldb23,
title = {{The Story of GraphLab - From Scaling Machine Learning to Shaping Graph Systems Research (VLDB 2023 Test-of-time Award Talk)}},
author = {Gonzalez, Joseph E. and Low, Yucheng},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {4138--4138},
doi = {10.14778/3611540.3611637},
url = {https://doi.org/10.14778/3611540.3611637},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 21 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.00056746237 |
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