Scalable Community Detection via Parallel Correlation Clustering
Summary: Scalable shared-memory framework for community detection via LambdaCC (modularity and correlation clustering) with generalized sequential and parallel implementations. Achieves high-quality clustering on unweighted/weighted graphs with billions of edges and yields large speedups (up to 28x vs sequential), improving speed–quality trade-offs. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jessica Shi (Massachusetts Institute of Technology)
- 2. Laxman Dhulipala (Massachusetts Institute of Technology)
- 3. David Eisenstat (Google)
- 4. Jakub Łącki (Google)
- 5. Vahab Mirrokni (Google)
BibTeX Citation
@article{shi_vldb21,
title = {{Scalable Community Detection via Parallel Correlation Clustering}},
author = {Shi, Jessica and Dhulipala, Laxman and Eisenstat, David and Łącki, Jakub and Mirrokni, Vahab},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2305--2313},
doi = {10.14778/3476249.3476282},
url = {https://doi.org/10.14778/3476249.3476282},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,102 | The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering | 2025 | VLDB | 5.093636e-05 |
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