mlsanityData to 14 Oct 2026

A Polyhedral Study of Lifted Multicuts

arXiv 2202.08068 ↗First posted 16 Feb 2022cs.DM cs.LG math.OCcited by 3

Not on the map. Why?

Abstract (arXiv)

Fundamental to many applications in data analysis are the decompositions of a graph, i.e. partitions of the node set into component-inducing subsets. One way of encoding decompositions is by multicuts, the subsets of those edges that straddle distinct components. Recently, a lifting of multicuts from a graph G = (V, E) to an augmented graph hat G = (V, E cup F) has been proposed in the field of image analysis, with the goal of obtaining a more expressive characterization of graph decompositions in which it is made explicit also for pairs F subseteq tbinomV2 setminus E of non-neighboring nodes whether these are in the same or distinct components. In this work, we study in detail the polytope in ℝE cup F whose vertices are precisely the characteristic vectors of multicuts of hat G lifted from G, connecting it, in particular, to the rich body of prior work on the clique partitioning and multilinear polytope.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 65 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

5 of 65 references.

References · 5

  1. 2014Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete and Complete k-partite Graphscited by 27
  2. 2015Efficient Decomposition of Image and Mesh Graphs by Lifted Multicutscited by 26
  3. 2014Coalition Structure Generation over Graphscited by 5
  4. 2016Correlation Clustering with Low-Rank Matricescited by 4
  5. 2019Solving unconstrained 0-1 polynomial programs through quadratic convex reformulationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.