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Planting Trees for scalable and efficient Canonical Hub Labeling

Summary: Canonical Hub Labeling at scale via PLaNT using collaborative label partitioning for in-memory labeling and parallel querying on massive graphs. On 72 threads, PLaNT is up to 47.4x faster than sequential PLL, yields 17% smaller labels on average, and achieves up to 9.5x speedups on a 64-node cluster vs paraPLL. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12251
Venue
VLDB
Year
2020
Pagerank
4.4190656e-05
Overall Rank
8,967 | 37.62%
DOI
10.14778/3372716.3372722

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