FSM: A Fine-grained Splitting and Merging Framework for Dual-balanced Graph Partition

Summary: Proposes FSM, a two-phase fine-grained splitting-and-merging framework that minimizes cut size while enforcing dual-balance (vertex and edge) and can integrate with existing single-balanced partitioners. Introduces Fast and Precise Merging for efficiency/quality trade-offs and reports SOTA cut reduction with up to ~18% downstream runtime savings on PageRank/CC/diameter. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13465
Venue: VLDB
Year: 2024
Pagerank: 4.1945683e-05
Overall Rank: 11,028 | 23.29%
DOI: 10.14778/3665844.3665864

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
4	Pregel: A System for Large-Scale Graph Processing	2010	SIGMOD	0.0019005923
686	Finding Maximal Cliques in Massive Networks by H*-graph	2010	SIGMOD	0.00018178029
1,968	An Experimental Comparison of Partitioning Strategies in Distributed Graph Processing	2017	VLDB	9.9071968e-05
2,595	LEOPARD: Lightweight Edge-Oriented Partitioning and Replication for Dynamic Graphs	2016	VLDB	8.4735292e-05
4,020	TopoX: Topology Refactorization for Efficient Graph Partitioning and Processing	2019	VLDB	6.5237459e-05
4,234	Distributed Edge Partitioning for Trillion-edge Graphs	2019	VLDB	6.3355073e-05
4,497	Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent	2019	VLDB	6.1387773e-05
4,867	Application Driven Graph Partitioning	2020	SIGMOD	5.8651797e-05
5,949	Hybrid Edge Partitioner: Partitioning Large Power-Law Graphs under Memory Constraints	2021	SIGMOD	5.2595857e-05

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