On the Sparsifiability of Correlation Clustering: Approximation Guarantees under Edge Sampling

TL;DR

This paper investigates how much edge information is necessary for correlation clustering to retain approximation guarantees, revealing a structural dichotomy based on pseudometric properties and establishing bounds on sparsification and robustness.

Contribution

It introduces a sparsification-approximation framework for correlation clustering, providing optimal coreset sizes, active triangle inequalities bounds, and a robust approximation algorithm under edge sampling.

Findings

Optimal size $ ilde{O}(n/ ext{epsilon}^2)$ coresets for clustering disagreement

At most $inom{n}{2}$ triangle inequalities active at LP vertices

Robust $rac{10}{3}$-approximation with $ ilde{ heta}(n^{3/2})$ edges observed

Abstract

Correlation Clustering (CC) is a fundamental unsupervised learning primitive whose strongest LP-based approximation guarantees require $\Theta(n^3)$ triangle inequality constraints and are prohibitive at scale. We initiate the study of \emph{sparsification--approximation trade-offs} for CC, asking how much edge information is needed to retain LP-based guarantees. We establish a structural dichotomy between pseudometric and general weighted instances. On the positive side, we prove that the VC dimension of the clustering disagreement class is exactly $n{-}1$, yielding additive $\varepsilon$-coresets of optimal size $\tilde{O}(n/\varepsilon^2)$; that at most $\binom{n}{2}$ triangle inequalities are active at any LP vertex, enabling an exact cutting-plane solver; and that a sparsified variant of LP-PIVOT, which imputes missing LP marginals via triangle inequalities, achieves a robust $\frac{10}{3}$-approximation (up to an additive term controlled by an empirically computable imputation-quality statistic $\overline{\Gamma}_w$) once $\tilde{\Theta}(n^{3/2})$ edges are observed, a threshold we prove is sharp. On the negative side, we show via Yao's minimax principle that without pseudometric structure, any algorithm observing $o(n)$ uniformly random edges incurs an unbounded approximation ratio, demonstrating that the pseudometric condition governs not only tractability but also the robustness of CC to incomplete information.