Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.
Machine Learning , volume =
3 Pith papers cite this work. Polarity classification is still indexing.
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Correlation Clustering admits polynomial kernels parameterized by k plus degeneracy or closure of the fuzzy edge graph.
Establishes n^{1-ε}-hardness of approximation for dichromatic number and acyclic number on tournaments, plus polynomial-time approximations for ℓ-dicolorable digraphs and special dense cases.
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Query Lower Bounds for Correlation Clustering under Memory Constraints
Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.
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Clustering with Locally Bounded Ignorance
Correlation Clustering admits polynomial kernels parameterized by k plus degeneracy or closure of the fuzzy edge graph.
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Hardness and Approximation for Coloring Digraphs
Establishes n^{1-ε}-hardness of approximation for dichromatic number and acyclic number on tournaments, plus polynomial-time approximations for ℓ-dicolorable digraphs and special dense cases.