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Triparts: Scalable Streaming Graph Partitioning to Enhance Community Structure

Summary: TriParts introduces triangle preservation as a streaming graph-partitioning objective, retaining community structure alongside balance and low replication. Bloom Filters, Triangle Map, and High Degree Map yield up to 4–8.3× more local triangles than DBH/HDRF on 1.6B-edge graphs. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14125
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,903 | 25.20%
DOI
10.14778/3746405.3746423

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BibTeX Citation

@article{bhoot_vldb25,
        title = {{Triparts: Scalable Streaming Graph Partitioning to Enhance Community Structure}},
        author = {Bhoot, Ruchi and Khare, Tuhin and Agarwal, Manoj and Jaiswal, Siddharth and Simmhan, Yogesh},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {9},
        pages = {2992--3006},
        doi = {10.14778/3746405.3746423},
        url = {https://doi.org/10.14778/3746405.3746423},
        year = {2025}
}

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