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A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms

Summary: Empirical study of graph partitioning for work-efficient analytics on large clusters up to 256 machines with Gluon. CVC outperforms Edge-Cut at scale despite higher replication, due to favorable communication patterns; supports multiple strategies and a simple decision tree. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11968
Venue
VLDB
Year
2019
Pagerank
4.5491792e-05
Overall Rank
8,254 | 42.58%
DOI
10.14778/3297753.3297754

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