$\rho(B) \leq \rho(|B|)$, where $\rho$ is the spectral radius Let $B_{d \times d}$ be an integral matrix and let $A$ be such that $a_{IJ} = |b_{IJ}|$, $1 \leq I, J \leq d$. Then, $\rho(B) \leq \rho(A)$. 
My strategy is to use Gelfand's formula, i.e., for a square matrix $M$ and a matrix norm $||\cdot||$, 
$$\rho(M) = \lim_{k \to \infty} ||M^k||^{\frac{1}{k}}.$$
If I manage to prove that for each pair $1 \leq I, J \leq d$, $(B^n)_{IJ} \leq (A^n)_{IJ}$, the result would follow. 
Indeed, for $n=1$ the result is trivially true. Now, suppose that it is valid for $n$. Then, (see problem below the displayed equations)
\begin{align*} (B^{n+1})_{IJ} =  (B^{n}B)_{IJ} &= \sum_{K=1}^d (B^{n})_{IK}(B)_{KJ}\\
&\leq \sum_{K=1}^d (A^{n})_{IK}(A)_{KJ}\\
&= (A^{n+1})_{IJ}.
\end{align*}
$\textbf{Problem!}$ Isn't that possible that the inequality in the last displayed set of equations does not hold? Because $(B^n)_{IK}$ and $B_{KJ}$ could be negative, for instance, we could have $(A^n)_{IK} = 1, (A)_{KJ} = 1$ and $(B^n)_{IK} = -2, (B)_{KJ} = -1$. My guess is that this cannot happen because $A = |B|$ (entrywise).
Any help is appreciated. 
 A: start by assuming WLOG that $A$ is strictly positive.
i.e. if needed consider $B':= \delta\mathbf {11}^T + B$ and $A':= \delta\mathbf {11}^T + A$ for any $\delta \gt 0$ and topological continuity of eigenvalues gives the results. 
main idea
Perron theory tells us there is some positive vector $\mathbf v$ such that
$A\mathbf v = \lambda_1 \mathbf v$  for $\lambda_1 \gt 0$ 
since similarity transforms preserve eigenvalues, use a well chosen similarity transform
$D := \text{diag}\big(\mathbf v\big)$ 
then consider
$\big(D^{-1}AD\big)\mathbf 1=D^{-1}A\big(D\mathbf 1\big) = D^{-1}A\mathbf v = \lambda_1  D^{-1}\mathbf v = \lambda_1 \mathbf 1$
so the one's vector is the Perron vector, and $\big(D^{-1}AD\big)$ has homogeneous row sums.  In the case of $\lambda_1=1$ this would be called a stochastic matrix.    
application of Gerschgorin discs tells us that
$\lambda_\text{max modulus}\big(D^{-1}BD\big)\leq \lambda_1\longrightarrow  \lambda_\text{max modulus}\big(B\big) \leq \lambda_\text{max modulus}\big(A\big) = \lambda_1$
and completes the exercise  
A: The idea of using Gelfand's formula indeed works. You may prove by mathematical induction that $|(B^n)_{ij}|\le(A^n)_{ij}$.
