Maximum Leaf Spanning Trees of Growing Sierpinski Networks Models

TL;DR

This paper develops recursive algorithms to find maximum leaf spanning trees in Sierpinski growing networks, aiding the analysis of their complex topological and dynamical properties.

Contribution

It introduces new recursive algorithms for MLS-trees in Sierpinski networks and a stochastic edge-cumulative distribution method showing power law behavior.

Findings

MLS-trees help identify kernels and dominating sets.

Edge-cumulative distribution follows a power law.

Algorithms facilitate analysis of large complex networks.

Abstract

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.