Correlation clustering for general graphs
Year: 2025
Type: article
Abstract: Correlation clustering provides a method for separating the vertices of a signed graph into the optimum number of clusters without specifying that number in advance. The main goal in this type of clustering is to minimize the number of disagreements: the number of negative edges inside clusters plus the number of positive edges between clusters. In this paper, we present an algorithm for correlation clustering in general case. Also, we show that there is a necessary and sufficient condition under which the lower bound, maximum number of edge disjoint weakly negative cycles, is equal to minimum number of disagreements. Finally, we prove that the presented algorithm gives a 2-approximation for a subclass of signed graphs.
Source: Discrete Mathematics
Author: Leila Parsaei-Majd
Institutions: Hasso Plattner Institute, University of Potsdam
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,320
OpenAlex ID: W4413793505