Stratified-Sampling over Social Networks Using MapReduce
Summary: Scalable MapReduce approach for stratified sampling on large online social networks. Proposes algorithms for single- and multi-survey stratified sampling with sharing constraints, plus MapReduce implementations and experimental evaluation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Roy Levin (IBM)
- 2. Yaron Kanza (Cornell University; Jacobs Technion-Cornell Innovation Institute; Technion)
BibTeX Citation
@inproceedings{levin_sigmod14,
title = {{Stratified-Sampling over Social Networks Using MapReduce}},
author = {Levin, Roy and Kanza, Yaron},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2588577},
url = {https://dl.acm.org/doi/10.1145/2588555.2588577},
year = {2014}
}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,178 | Optimal Sampling From Distributed Streams | 2010 | PODS | 9.0159538e-05 |
| 3,096 | Early Accurate Results for Advanced Analytics on MapReduce | 2012 | VLDB | 7.7629371e-05 |
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