  Given any square matrix, $\mathbf{M}$, whose diagonal elements are negative,
and which is multiplied by a variable, $\sigma$, we wish to find the minimal
$\sigma$ such that the eigenvalue of $\mathbf{M}_{\sigma}$ is exactly zero. By
Gershgorin, we know that $\mathbf{M}_{\sigma}$ can be made stable by making
$\sigma$ large enough. We prove a relation which analytically determines when
and how we are able to find the value of $\sigma$ such that the maximal
eigenvalue is exactly zero. In so doing, we prove the equivalence of the roots
of the characteristic polynomial of $\mathbf{M}_{\sigma}$ and the eigenvalues
that arise from a scaling operation on $\mathbf{M}$. Further, through the
characteristic polynomial, we are able to isolate the dominant feedback cycles
comprising the elements of the matrix which, under the action of $\sigma$,
ensures the stability of the system. We then explore, using the stabilising and
destabilising cycles within the coefficients of the characteristic polynomial,
an intrinsic spectral signature associated with any matrix based on the size
and sign (or zero) of its respective elements.
