Sparse matrices: convergence of the characteristic polynomial seen from infinity

TL;DR

This paper studies the spectral properties of sparse random matrices with Bernoulli entries, showing convergence of their characteristic polynomials to complex random functions and deriving eigenvalue asymptotics, extending to semi-sparse regimes.

Contribution

It introduces a novel convergence result for the characteristic polynomial of sparse matrices and provides simplified proofs for eigenvalue behaviors in Erdős-Rényi digraphs.

Findings

Eigenvalues near $d$ for $d>1$

Second eigenvalue below $\sqrt{d}$ for $d>1$

Eigenvalues form a Poisson process for $d<1$

Abstract

We prove that the reverse characteristic polynomial $\det(I_n - zA_n)$ of a random $n \times n$ matrix $A_n$ with iid $\mathrm{Bernoulli}(d/n)$ entries converges in distribution towards the random infinite product $\prod_{\ell = 1}^\infty(1-z^\ell)^{Y_\ell}$ where $Y_\ell$ are independent $\mathrm{Poisson}(d^\ell/\ell)$ random variables. We show that this random function is a Poisson analog of more classical Gaussian objects such as the Gaussian holomorphic chaos. As a byproduct, we obtain new simple proofs of previous results on the asymptotic behaviour of extremal eigenvalues of sparse Erd\H{o}s-R\'enyi digraphs: for every $d>1$, the greatest eigenvalue of $A_n$ is close to $d$ and the second greatest is smaller than $\sqrt{d}$, a Ramanujan-like property for irregular digraphs. For $d<1$, the only non-zero eigenvalues of $A_n$ converge to a Poisson multipoint process on the unit circle. Our results also extend to the semi-sparse regime where $d$ is allowed to grow to $\infty$ with $n$, slower than $n^{o(1)}$. We show that the reverse characteristic polynomial converges towards a more classical object written in terms of the exponential of a log-correlated real Gaussian field, as in the dense case studied in a recent paper \cite{bordenave2020convergence}. In the semi-sparse regime, the empirical spectral distribution of $A_n/\sqrt{d_n}$ converges to the circle distribution; as a consequence of our results, the second eigenvalue sticks to the edge of the circle.