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Growing models from deterministic to random hierarchical networks

Growing models from deterministic to random hierarchical networks In this paper, we introduce a growing hierarchical network model with a tunable parameter q which is an economic model based on the real-life networks of profit distributions. Our theoretical results indicate that the model follows a power-law degree distribution P(k) ∝ k−γ with the power-law exponent of γ ≈ 1. Besides, the model has a smaller Average Path Length (APL) and a larger clustering coefficient for smaller q, proved to be a Small-World Network (SWN). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Systems, Control and Communications Inderscience Publishers

Growing models from deterministic to random hierarchical networks

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1755-9340
eISSN
1755-9359
DOI
10.1504/IJSCC.2009.026318
Publisher site
See Article on Publisher Site

Abstract

In this paper, we introduce a growing hierarchical network model with a tunable parameter q which is an economic model based on the real-life networks of profit distributions. Our theoretical results indicate that the model follows a power-law degree distribution P(k) ∝ k−γ with the power-law exponent of γ ≈ 1. Besides, the model has a smaller Average Path Length (APL) and a larger clustering coefficient for smaller q, proved to be a Small-World Network (SWN).

Journal

International Journal of Systems, Control and CommunicationsInderscience Publishers

Published: Jan 1, 2009

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