A Faster Algorithm for Constrained Correlation Clustering

TL;DR

This paper presents a faster approximation algorithm for Constrained Correlation Clustering that runs in near-cubic time and achieves a 16-approximation, improving computational efficiency over previous methods.

Contribution

It introduces a significantly faster algorithm for Constrained Correlation Clustering with a slightly weaker approximation factor, and provides a derandomization of the CC-PIVOT algorithm.

Findings

Algorithm runs in (n^3) time

Achieves a 16-approximation factor

Provides a derandomization of CC-PIVOT

Abstract

In the Correlation Clustering problem we are given $n$ nodes, and a preference for each pair of nodes indicating whether we prefer the two endpoints to be in the same cluster or not. The output is a clustering inducing the minimum number of violated preferences. In certain cases, however, the preference between some pairs may be too important to be violated. The constrained version of this problem specifies pairs of nodes that must be in the same cluster as well as pairs that must not be in the same cluster (hard constraints). The output clustering has to satisfy all hard constraints while minimizing the number of violated preferences. Constrained Correlation Clustering is APX-Hard and has been approximated within a factor 3 by van Zuylen et al. [SODA '07] using $\Omega(n^{3\omega})$ time. In this work, using a more combinatorial approach, we show how to approximate this problem significantly faster at the cost of a slightly weaker approximation factor. In particular, our algorithm runs in $\widetilde{O}(n^3)$ time and approximates Constrained Correlation Clustering within a factor 16. To achieve our result we need properties guaranteed by a particular influential algorithm for (unconstrained) Correlation Clustering, the CC-PIVOT algorithm. This algorithm chooses a pivot node $u$, creates a cluster containing $u$ and all its preferred nodes, and recursively solves the rest of the problem. As a byproduct of our work, we provide a derandomization of the CC-PIVOT algorithm that still achieves the 3-approximation; furthermore, we show that there exist instances where no ordering of the pivots can give a $(3-\varepsilon)$-approximation, for any constant $\varepsilon$. Finally, we introduce a node-weighted version of Correlation Clustering, which can be approximated within factor 3 using our insights on Constrained Correlation Clustering.