Streaming max-cut requires Ω(n) space for dense graphs but Ω(n log(ε² n)/ε²) space for graphs with Θ(n/ε²) edges when outputting the cut, with matching upper bounds for dense case and similar separations for densest subgraph.
A survey on the densest subgraph problem and its variants,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Hybrid GBS with classical post-processing for DkSP achieves near-optimal solutions and ~4X sampling efficiency gains on community graphs while outperforming pure post-selection on sparse graphs.
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Streaming Complexity Separations for Dense and Sparse Graphs
Streaming max-cut requires Ω(n) space for dense graphs but Ω(n log(ε² n)/ε²) space for graphs with Θ(n/ε²) edges when outputting the cut, with matching upper bounds for dense case and similar separations for densest subgraph.
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Towards a Hybrid Quantum Enhanced Solution for Densest k-Subgraph Problem
Hybrid GBS with classical post-processing for DkSP achieves near-optimal solutions and ~4X sampling efficiency gains on community graphs while outperforming pure post-selection on sparse graphs.