Research Data Engineering

Canonical Cut Hierarchy in Almost-Linear Time via Reduction to Max-Flow

Nov 5, 2026 Source ↗

Key Insights

The canonical cut hierarchy recursively partitions a graph along Tutte-Nash-Williams partitions, choosing the partition with the most number of parts at each step.
We give a randomized almost linear-t.

DataOps

Cite this synthesis DOI: 10.48550/arXiv.2611.08466 ↗

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@misc{ acaciadata-research-canonical-cut-hierarchy-in-almost-linear-time-via-reduction,
  title = { Canonical Cut Hierarchy in Almost-Linear Time via Reduction to Max-Flow },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2611.08466 },
  url = { http://arxiv.org/abs/2611.08466v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Canonical Cut Hierarchy in Almost-Linear Time via Reduction to Max-Flow
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2611.08466
UR  - http://arxiv.org/abs/2611.08466v1
ER  -

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Overview

The canonical cut hierarchy recursively partitions a graph along Tutte-Nash-Williams partitions, choosing the partition with the most number of parts at each step. We give a randomized almost linear-t

Key Topics

Distributed Systems: A theme covered by this item within the data engineering context.

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Article Metadata

Evergreen Synthesized

distributed-systems dataops

DataOps

SQI 0.750