  The dynamical phenomena of complex networks are very difficult to predict
from local information due to the rich microstructures and corresponding
complex dynamics. On the other hands, it is a horrible job to compute some
stochastic parameters of a large network having thousand and thousand nodes. We
design several recursive algorithms for finding spanning trees having maximal
leaves (MLS-trees) in investigation of topological structures of Sierpinski
growing network models, and use MLS-trees to determine the kernels, dominating
and balanced sets of the models. We propose a new stochastic method for the
models, called the edge-cumulative distribution, and show that it obeys a power
law distribution.
