Mapping Koch curves into scale-free small-world networks

Abstract

The class of Koch fractals is one of the most interesting families of fractals, and the study of complex networks is a central issue in the scientific community. In this paper, inspired by the famous Koch fractals, we propose a mapping technique converting Koch fractals into a family of deterministic networks, called Koch networks. This novel class of networks incorporates some key properties characterizing a majority of real-life networked systems---a power-law distribution with exponent in the range between 2 and 3, a high clustering coefficient, small diameter and average path length, and degree correlations. Besides, we enumerate the exact numbers of spanning trees, spanning forests, and connected spanning subgraphs in the networks. All these features are obtained exactly according to the proposed generation algorithm of the networks considered. The network representation approach could be used to investigate the complexity of some real-world systems from the perspective of complex networks.

Definitive version accepted for publication in Journal of Physics A

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Cited by in corpus (3)

• Counting spanning trees in self-similar networks by evaluating determinants
• Impact of degree heterogeneity on the behavior of trapping in Koch networks
• Scaling of mean first-passage time as efficiency measure of nodes sending information on scale-free Koch networks