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

Abstract: A $(\phi,\epsilon)$-expander-decomposition of a graph $G$ (with $n$ vertices and $m$ edges) is a partition of $V$ into clusters $V_1,\ldots,V_k$ with conductance $\Phi(G[V_i]) \ge \phi$, such that there are at most $\epsilon m$ inter-cluster edges. Such a decomposition plays a crucial role in many graph algorithms. We give a randomized $\tilde{O}(m/\phi)$ time algorithm for computing a $(\phi, \phi\log^2 {n})$-expander decomposition. This improves upon the $(\phi, \phi\log^3 {n})$-expander decomposition also obtained in $\tilde{O}(m/\phi)$ time by Saranurak and Wang, SODA 2019 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: 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. 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.