Experimental Analysis of Streaming Algorithms for Graph Partitioning
Summary: Systematic study of streaming graph partitioning across edge-cut and vertex-cut for analytics and online workloads. Finds that a no-partitioning baseline often wins; partitioning choice depends on graph type, workload, and application requirements. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Anil Pacaci (University of Waterloo)
- 2. M. Tamer Özsu (University of Waterloo)
BibTeX Citation
@inproceedings{pacaci_sigmod19,
title = {{Experimental Analysis of Streaming Algorithms for Graph Partitioning}},
author = {Pacaci, Anil and Özsu, M. Tamer},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300076},
url = {https://dl.acm.org/doi/10.1145/3299869.3300076},
year = {2019}
}
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