Expander Decomposition with Fewer Inter-Cluster Edges Using a Spectral Cut Player

Abstract

A $(\ddot{I}•,\hat{I}\mu )$-expander-decomposition of a graph $G$ (with $n$ vertices and $m$ edges) is a partition of $V$ into clusters $V_1,\dots ,V_k$ with conductance $\hat{I}¦(G[V_i])\ge \ddot{I}•$, such that there are at most $\hat{I}\mu m$ inter-cluster edges. Such a decomposition plays a crucial role in many graph algorithms. We give a randomized $\tilde{O}(m/\ddot{I}•)$ time algorithm for computing a $(\ddot{I}•,\ddot{I}•{\log }^{2}n)$-expander decomposition. This improves upon the $(\ddot{I}•,\ddot{I}•{\log }^{3}n)$-expander decomposition also obtained in $\tilde{O}(m/\ddot{I}•)$ 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 cut, but continues to play on a trimmed part of the large side. The crux of our improvement is the design of a non-stop version of the cleverer cut player of [Orecchia, Schulman, Vazirani, Vishnoi, STOC 2008] (OSVV). The cut player of OSSV uses a more sophisticated random walk, a subtle potential function, and spectral arguments. Designing and analysing a non-stop version of this game was an explicit open question asked by SW.

65 pages; Improved (simplified) presentation