Words Maps and Spectra of Random Graph Lifts

TL;DR

This paper analyzes formal words and their associated permutation fixed points to improve bounds on eigenvalues of random graph lifts, introducing a new word categorization and providing insights into spectral properties.

Contribution

It introduces a new categorization of formal words, extends bounds on eigenvalues of random graph lifts, and offers a simplified proof of a known distribution theorem.

Findings

Improved upper bound on new eigenvalues: O(L^{1/3} R^{2/3})

New categorization of words extending primitive/imprimitive dichotomy

Simplified proof of the distribution of fixed points for fixed words

Abstract

We begin with a new analysis of formal words. Let w be a formal word in letters g_1,...,g_k. The word map associated with w maps the permutations s_1,...,s_k in S_n to the permutation obtained by replacing for each i, every occurrence of g_i in w by s_i. We investigate the random variable X_w^n that counts the fixed points in this permutation when the s_i are selected uniformly at random. A major ingredient of our work is a new categorization of words which considerably extends the dichotomy of primitive vs. imprimitive words. We establish some results and make a few conjectures about the relation between the expectation E(X_w^n) and this new categorization. This analysis contributes deeply to our study of the spectra of random lifts of graphs. Let G be a connected graph, and let the infinite tree T be its universal cover space. If L and R are the spectral radii of G and T respectively, then, as shown by J. Friedman, for almost every n-lift H of G, all "new" eigenvalues of H are < O(L^(1/2)R^(1/2)). We improve this upper bound to O(L^(1/3)R^(2/3)), and our aforementioned conjectures suggest a possible approach to proving an upper bound of O(R). This is a generalization of the problem of bounding the second eigenvalue in a random 2d-regular graph. As an aside, we obtain a new conceptual and relatively simple proof of a theorem of A. Nica, which determines, for every fixed w, the limit distribution (as n \to \infty) of X_w^n. A surprising aspect of this theorem is that the answer depends only on the largest integer d so that w=u^d for some word u.