Experimental Analysis of Streaming Algorithms for Graph Partitioning
Summary: Systematic study of streaming graph partitioning across edge-cut and vertex-cut for analytics and online workloads. Finds that a no-partitioning baseline often wins; partitioning choice depends on graph type, workload, and application requirements. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Anil Pacaci
- 2. M. Tamer Özsu
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| 8,254 | A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms | 2019 | VLDB | 4.5491792e-05 |
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| 2,494 | Streaming Graph Partitioning: An Experimental Study | 2018 | VLDB | 8.6508229e-05 |