The Spectral Norm of Random Lifts of Matrices

Abstract

We study the spectral norm of random lifts of matrices. Given an $n\times n$ symmetric matrix $A$, and a centered distribution $\pi$ on $k\times k\ (k\ge 2)$ symmetric matrices with spectral norm at most $1$, let the matrix random lift $A^{(k,\pi)}$ be the random symmetric $kn\times kn$ matrix $(A_{ij}X_{ij})_{1\le i < j \le n}$, where $X_{ij}$ are independent samples from $\pi$. We prove that $$\mathbb{E} \|A^{(k,\pi)}\|\lesssim \max_{i}\sqrt{\sum_j A_{ij}^2}+\max_{ij}|A_{ij}|\sqrt{\log (kn)}.$$ This result can be viewed as an extension of existing spectral bounds on random matrices with independent entries, providing further instances where the multiplicative $\sqrt{\log n}$ factor in the Non-Commutative Khintchine inequality can be removed. As a direct application of our result, we prove an upper bound of $2(1+\epsilon)\sqrt{\Delta}+O(\sqrt{\log(kn)})$ on the new eigenvalues for random $k$-lifts of a fixed $G = (V,E)$ with $|V| = n$ and maximum degree $\Delta$, compared to the previous result of $O(\sqrt{\Delta\log(kn)})$ by Oliveira and the recent breakthrough by Bordenave and Collins which gives $2\sqrt{\Delta-1} + o(1)$ as $k\rightarrow\infty$ for $\Delta$-regular graph $G$.