Open AccessResearch on the deterministic complex network model based on the Sierpinski network
Author(s) -
Changming Xing,
Liu Fang-ai
Publication year - 2010
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.59.1608
Subject(s) - clustering coefficient , computer science , complex network , sierpinski triangle , average path length , degree distribution , fractal , theoretical computer science , degree (music) , complex system , scale free network , cluster analysis , network model , interdependent networks , artificial intelligence , mathematics , graph , shortest path problem , mathematical analysis , physics , world wide web , acoustics
In the last few years, the complex network has received considerable attention. It is proven that the small-word effect and scale-free property exist in various real-life networks. In this paper, based on the deterministic fractal—the Sierpinski gasket, two deterministic complex network evolving models, S-DSWN and S-DSFN, are proposed by iterative approach. S-DSWN can generate small-world network, while S-DSFN can generate scale-free networks. The iterative algorithms to generate the models are also designed. Then, some relevant characteristics of the networks, such as degree distribution, clustering coefficient, and diameter, are computed or predicted analytically, which match well with the characterizations of various real-life networks. Finally, an integrated model is introduced to unify S-DSWN and S-DSFN into the same framework, which makes it convenient to study the complexity of the real networked systems within the framework of complex network theory. Moreover, we have proven that these network models are maximal planar graphs.