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)
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Authors
- 1. Ruchi Bhoot (Indian Institute of Science)
- 2. Tuhin Khare (Georgia Institute of Technology)
- 3. Manoj Agarwal (GiKA.AI)
- 4. Siddharth Jaiswal (Indian Institute of Technology)
- 5. Yogesh Simmhan (Indian Institute of Science)
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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