Work

Breaking 3-Factor Approximation for Correlation Clustering in Polylogarithmic Rounds

Year: 2024

Type: conference-paper

Abstract: In this paper, we study parallel algorithms for the correlation clustering problem, where every pair of two different entities is labeled with similar or dissimilar. The goal is to partition the entities into clusters to minimize the number of disagreements with the labels. Currently, all efficient parallel algorithms have an approximation ratio of at least 3. In comparison with the 1.994 + ɛ ratio achieved by polynomial-time sequential algorithms [25], a significant gap exists.

Source: Society for Industrial and Applied Mathematics eBooks

DOI: 10.1137/1.9781611977912.143

Authors: Nairen Cao, Shang-En Huang, Hsin-Hao SU

Institution: Boston College

Language: English

Topic: Complexity and Algorithms in Graphs

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: AF: Small: Combinatorial Optimization Problems in Vertex Centric Computation