Infinite Lewis Weights in Spectral Graph Theory

TL;DR

This paper investigates the spectral effects of re-weighting graphs using $ ext{ell}_ ext{infty}$-Lewis weights, providing approximation guarantees for ER-minimization, efficient algorithms, and insights into spectral properties.

Contribution

It introduces a new local polarization technique for trees, establishes approximation bounds for Lewis weights in ER-minimization, and improves algorithmic efficiency to input-sparsity time.

Findings

$ ext{ell}_ ext{infty}$-Lewis weights approximate ER-minimization on trees within a factor of 3.12.

Provides upper bounds on approximation ratios for general graphs based on graph properties.

Algorithms run in input-sparsity time, significantly faster than previous SDP-based methods.

Abstract

We study the spectral implications of re-weighting a graph by the $\ell_\infty$-Lewis weights of its edges. Our main motivation is the ER-Minimization problem (Saberi et al., SIAM'08): Given an undirected graph $G$, the goal is to find positive normalized edge-weights $w\in \mathbb{R}_+^m$ which minimize the sum of pairwise \emph{effective-resistances} of $G_w$ (Kirchhoff's index). By contrast, $\ell_\infty$-Lewis weights minimize the \emph{maximum} effective-resistance of \emph{edges}, but are much cheaper to approximate, especially for Laplacians. With this algorithmic motivation, we study the ER-approximation ratio obtained by Lewis weights. Our first main result is that $\ell_\infty$-Lewis weights provide a constant ($\approx 3.12$) approximation for ER-minimization on \emph{trees}. The proof introduces a new technique, a local polarization process for effective-resistances ($\ell_2$-congestion) on trees, which is of independent interest in electrical network analysis. For general graphs, we prove an upper bound $\alpha(G)$ on the approximation ratio obtained by Lewis weights, which is always $\leq \min\{ \text{diam}(G), \kappa(L_{w_\infty})\}$, where $\kappa$ is the condition number of the weighted Laplacian. All our approximation algorithms run in \emph{input-sparsity} time $\tilde{O}(m)$, a major improvement over Saberi et al.'s $O(m^{3.5})$ SDP for exact ER-minimization. Finally, we demonstrate the favorable effects of $\ell_\infty$-LW reweighting on the \emph{spectral-gap} of graphs and on their \emph{spectral-thinness} (Anari and Gharan, 2015). En-route to our results, we prove a weighted analogue of Mohar's classical bound on $\lambda_2(G)$, and provide a new characterization of leverage-scores of a matrix, as the gradient (w.r.t weights) of the volume of the enclosing ellipsoid.