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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)

Paper ID
5669
Venue
SIGMOD
Year
2019
Pagerank
7.4833791e-05
Overall Rank
3,362 | 76.94%
DOI
10.1145/3299869.3300076

Incoming Non-self Citations Over Time

Authors

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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