Approximating Small Sparse Cuts

TL;DR

This paper develops polylogarithmic approximation algorithms for small sparse cuts and related problems, extending sample set techniques and introducing a local cut-matching game approach.

Contribution

It introduces new approximation algorithms for sparse cuts based on extended sample set methods and a novel local cut-matching game framework.

Findings

Achieves $ ext{O}( ext{polylog}\, k)$-approximation for sparse cuts.

Provides an $ ext{O}( ext{log}\, opt)$-approximation for min-max graph partitioning.

Improves bounds on multicut mimicking networks.

Abstract

We study polynomial-time approximation algorithms for (edge/vertex) Sparsest Cut and Small Set Expansion in terms of $k$, the number of edges or vertices cut in the optimal solution. Our main results are $\mathcal{O}(\text{polylog}\, k)$-approximation algorithms for various versions in this setting. Our techniques involve an extension of the notion of sample sets (Feige and Mahdian STOC'06), originally developed for small balanced cuts, to sparse cuts in general. We then show how to combine this notion of sample sets with two algorithms, one based on an existing framework of LP rounding and another new algorithm based on the cut-matching game, to get such approximation algorithms. Our cut-matching game algorithm can be viewed as a local version of the cut-matching game by Khandekar, Khot, Orecchia and Vishnoi and certifies an expansion of every vertex set of size $s$ in $\mathcal{O}(\log s)$ rounds. These techniques may be of independent interest. As corollaries of our results, we also obtain an $\mathcal{O}(\log opt)$-approximation for min-max graph partitioning, where $opt$ is the min-max value of the optimal cut, and improve the bound on the size of multicut mimicking networks computable in polynomial time.