Correlation Clustering and Two-edge-connected Augmentation for Planar Graphs

Philip N. Klein, Claire Mathieu, Hang Zhou, Marc Herbstritt
2015 Symposium on Theoretical Aspects of Computer Science  
In correlation clustering, the input is a graph with edge-weights, where every edge is labelled either + or − according to similarity of its endpoints. The goal is to produce a partition of the vertices that disagrees with the edge labels as little as possible. In two-edge-connected augmentation, the input is a graph with edge-weights and a subset R of edges of the graph. The goal is to produce a minimum weight subset S of edges of the graph, such that for every edge in R, its endpoints are
more » ... edge-connected in R ∪ S. For planar graphs, we prove that correlation clustering reduces to two-edge-connected augmentation, and that both problems have a polynomial-time approximation scheme.
doi:10.4230/lipics.stacs.2015.554 dblp:conf/stacs/KleinMZ15 fatcat:7h7w2543qbdbxixjf25ijziacq