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Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent

Summary: Introduces scalable multi-dimensional balanced graph partitioning, enforcing multiple weight constraints crucial for distributed workloads. Randomized projected gradient descent on a nonconvex relaxation, with efficient projection, outperforms prior methods on graphs up to hundreds of billions of edges. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12202
Venue
VLDB
Year
2019
Pagerank
6.5575113e-05
Overall Rank
4,697 | 67.78%
DOI
10.14778/3324301.3324307

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{avdiukhin_vldb19,
        title = {{Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent}},
        author = {Avdiukhin, Dmitrii and Pupyrev, Sergey and Yaroslavtsev, Grigory},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {8},
        pages = {906--919},
        doi = {10.14778/3324301.3324307},
        url = {https://doi.org/10.14778/3324301.3324307},
        year = {2019}
}

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