Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud
Summary: Distributed GraphLab brings asynchronous, dynamic graph-parallel ML to clusters with strong consistency, using pipelined locking/versioning to curb communication and Chandy–Lamport snapshots for fault tolerance. Demonstrates 10–100× speedups over Hadoop. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yucheng Low (Carnegie Mellon University)
- 2. Joseph Gonzalez (Carnegie Mellon University)
- 3. Aapo Kyrola (Carnegie Mellon University)
- 4. Danny Bickson (Carnegie Mellon University)
- 5. Carlos Guestrin (Carnegie Mellon University)
- 6. Joseph M. Hellerstein (University of California Berkeley)
BibTeX Citation
@article{low_vldb12,
title = {{Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud}},
author = {Low, Yucheng and Gonzalez, Joseph and Kyrola, Aapo and Bickson, Danny and Guestrin, Carlos and Hellerstein, Joseph M.},
journal = {PVLDB},
series = {{VLDB} '12},
volume = {5},
number = {8},
pages = {716--727},
doi = {10.14778/2212351.2212354},
url = {https://doi.org/10.14778/2212351.2212354},
year = {2012}
}Incoming Citations (Sorted by Pagerank)
Showing 24 of 124 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012089097 |
| 464 | An Architecture for Parallel Topic Models | 2010 | VLDB | 0.0001776101 |
| 1,133 | Large Graph Processing in the Cloud | 2010 | SIGMOD | 0.00011867467 |
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