The sparse circular law under minimal assumptions

The circular law asserts that the empirical distribution of eigenvalues of appropriately normalized $n\times n$ matrix with i.i.d. entries converges to the uniform measure on the unit disc as the dimension $n$ grows to infinity. Consider an $n\times n$ matrix $A_n=(\delta_{ij}^{(n)}\xi_{ij}^{(n)})$, where $\xi_{ij}^{(n)}$ are copies of a real random variable of unit variance, variables $\delta_{ij}^{(n)}$ are Bernoulli ($0/1$) with ${\mathbb P}\{\delta_{ij}^{(n)}=1\}=p_n$, and $\delta_{ij}^{(n)}$ and $\xi_{ij}^{(n)}$, $i,j\in[n]$, are jointly independent. In order for the circular law to hold for the sequence $\big(\frac{1}{\sqrt{p_n n}}A_n\big)$, one has to assume that $p_n n\to \infty$. We derive the circular law under this minimal assumption.