Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.
Journal of the ACM (JACM) , volume =
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
representative citing papers
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
Connectivity-preserving important separators of size at most k number 2^{O(k log k)} and can be enumerated in the same bound, yielding 2^{O(k log k)} FPT time for constant-class Node Multiway Cut-Uncut.
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.
Revenue maximization for pricing datasets to budget-constrained buyers is APX-hard, with a 2-approximation for online arrivals and a (1-1/e)^{-1}-approximation for offline.
citing papers explorer
-
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.
-
Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
-
Connectivity-Preserving Important Separators: A Framework for Cut-Uncut Problems
Connectivity-preserving important separators of size at most k number 2^{O(k log k)} and can be enumerated in the same bound, yielding 2^{O(k log k)} FPT time for constant-class Node Multiway Cut-Uncut.
-
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.
-
Revenue-Optimal Pricing for Budget-Constrained Buyers in Data Markets
Revenue maximization for pricing datasets to budget-constrained buyers is APX-hard, with a 2-approximation for online arrivals and a (1-1/e)^{-1}-approximation for offline.