Randomized LOCAL algorithm computes 2-ruling sets in O(log log n) rounds w.h.p. on graphs with arboricity O(log log n), nearly matching lower bounds and exponentially improving prior combinations of results.
Partitioning hypergraphs is hard: Models, inapproximability, and applications
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
GPU algorithm for hypergraph partitioning with size and distinct hyperedge constraints achieves 380x speedup and 1.2-2.0x better connectivity than sequential methods.
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Near-Optimal Distributed 2-Ruling Sets on Graphs with Low Arboricity
Randomized LOCAL algorithm computes 2-ruling sets in O(log log n) rounds w.h.p. on graphs with arboricity O(log log n), nearly matching lower bounds and exponentially improving prior combinations of results.
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Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints
GPU algorithm for hypergraph partitioning with size and distinct hyperedge constraints achieves 380x speedup and 1.2-2.0x better connectivity than sequential methods.