Expander Decomposition with Fewer Inter-Cluster Edges Using a Spectral Cut Player
Daniel Agassy (Tel Aviv University), Dani Dorfman (Max Planck, Institute for Informatics), Haim Kaplan (Tel Aviv University)

TL;DR
This paper introduces a faster randomized algorithm for expander decomposition that reduces inter-cluster edges, utilizing a novel non-stop cut player approach based on spectral methods, improving previous bounds.
Contribution
It develops a non-stop cut player for the spectral cut game, enabling more efficient expander decompositions with fewer inter-cluster edges, answering an open question from prior work.
Findings
Achieves a $(, ext{log}^2 n)$-expander decomposition in $ ilde{O}(m/)$ time.
Reduces inter-cluster edges to within a logarithmic factor of optimal.
Improves upon previous $(, ext{log}^3 n)$ bounds by Saranurak and Wang.
Abstract
A -expander-decomposition of a graph (with vertices and edges) is a partition of into clusters with conductance , such that there are at most inter-cluster edges. Such a decomposition plays a crucial role in many graph algorithms. We give a randomized time algorithm for computing a -expander decomposition. This improves upon the -expander decomposition also obtained in time by [Saranurak and Wang, SODA 2019] (SW) and brings the number of inter-cluster edges within logarithmic factor of optimal. One crucial component of SW's algorithm is non-stop version of the cut-matching game of [Khandekar, Rao, Vazirani, JACM 2009] (KRV): The cut player does not stop when it gets from the matching player an unbalanced sparse…
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