Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete and Complete k-partite Graphs

Abstract

We give new rounding schemes for the standard linear programming relaxation of the correlation clustering problem, achieving approximation factors almost matching the integrality gaps: - For complete graphs our appoximation is $2.06-\epsilon$ for a fixed constant $\epsilon$, which almost matches the previously known integrality gap of $2$. - For complete $k$-partite graphs our approximation is $3$. We also show a matching integrality gap. - For complete graphs with edge weights satisfying triangle inequalities and probability constraints, our approximation is $1.5$, and we show an integrality gap of $1.2$. Our results improve a long line of work on approximation algorithms for correlation clustering in complete graphs, previously culminating in a ratio of $2.5$ for the complete case by Ailon, Charikar and Newman (JACM'08). In the weighted complete case satisfying triangle inequalities and probability constraints, the same authors give a $2$-approximation; for the bipartite case, Ailon, Avigdor-Elgrabli, Liberty and van Zuylen give a $4$-approximation (SICOMP'12).

References in corpus (1)

• Correlation Clustering with Noisy Partial Information

Cited by in corpus (1)

• Motif Clustering and Overlapping Clustering for Social Network Analysis