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Streaming Graph Partitioning: An Experimental Study

Summary: Taxonomy and experiments on online streaming graph partitioning using a unified Flink runtime. Findings: low-cut partitioners excel for communication-heavy workloads but incur higher costs; model-agnostic approaches favor locality with lower costs in data-parallel graphs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11644
Venue
VLDB
Year
2018
Pagerank
8.6508229e-05
Overall Rank
2,494 | 82.66%
DOI
10.14778/3236187.3236208

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