Sparse matrices: convergence of the characteristic polynomial seen from infinity

Abstract

We prove that the reverse characteristic polynomial $\det (I_n-z{A}_n)$ 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 Erdős-Rényi 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.

Added the semi-sparse case $d_n\to \infty$ and a more complete description of the $d_n=d<1$ case