  The scaling properties of spectra of real world complex networks are studied
by using the wavelet transform. It is found that the spectra of networks are
multifractal. According to the values of the long-range correlation exponent,
the Hust exponent $H$, the networks can be classified into three types, namely,
$H>0.5$, $H=0.5$ and $H<0.5$. All real world networks considered belong to the
class of $H \ge 0.5$, which may be explained by the hierarchical properties.
