In this paper, we study the effect of sparsity on the appearance of outliers in the semi-circular law. Let $(W_n)_{n=1}^\infty$ be a sequence of random symmetric matrices such that each $W_n$ is $n\times n$ with i.i.d entries above and on the main diagonal equidistributed with the product $b_nξ$, where $ξ$ is a real centered uniformly bounded random variable of unit variance and $b_n$ is an independent Bernoulli random variable with a probability of success $p_n$. Assuming that $\lim\limits_{n\to\infty}n p_n=\infty$, we show that for the random sequence $(ρ_n)_{n=1}^\infty$ given by $$ρ_n:=θ_n+\frac{n p_n}{θ_n},\quad θ_n:=\sqrt{\max\big(\max\limits_{i\leq n}\|{\rm Row_i}(W_n)\|_2^2-np_n,n p_n\big)},$$ the ratio $\frac{\|W_n\|}{ρ_n}$ converges to one in probability. A non-centered counterpart of the theorem allows to obtain asymptotic expressions for eigenvalues of the Erdős--Renyi graphs, which were unknown in the regime $n p_n=Θ(\log n)$. In particular, denoting by $A_n$ the adjacency matrix of $\mathcal{G}(n,p_n)$ and by $λ_{|k|}(A_n)$ its $k$-th largest (by the absolute value) eigenvalue, under the assumptions $\lim\limits_{n\to\infty }n p_n=\infty$ and $\lim\limits_{n\to\infty}p_n=0$ we have: -(No non-trivial outliers) If $\liminf\frac{n p_n}{\log n}\geq\frac{1}{\log (4/e)}$ then for any fixed $k\geq2$, $\frac{|λ_{|k|}(A_n)|}{2\sqrt{n p_n}}$ converges to $1$ in probability. -(Outliers) If $\limsup\frac{n p_n}{\log n}<\frac{1}{\log (4/e)}$ then there is $\varepsilon>0$ such that for any $k\in\mathbb{N}$, we have $\lim\limits_{n\to\infty}\mathbb{P}\Big\{\frac{|λ_{|k|}(A_n)|}{2\sqrt{n p_n}}>1+\varepsilon\Big\}=1$. On a conceptual level, our result highlights similarities in appearance of outliers in spectrum of sparse matrices and the so-called BBP phase transition phenomenon in deformed Wigner matrices.