Realistic network growth using only local information: From random to scale-free and beyond

Thumbnail Image

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

We introduce a simple one-parameter network growth algorithm which is able to reproduce a wide variety of realistic network structures but without having to invoke any global information about node degrees such as preferential-attachment probabilities. Scale-free networks arise at the transition point between quasi-random and quasi-ordered networks. We provide a detailed formalism which accurately describes the entire network range, including this critical point. Our formalism is built around a statistical description of the inter-node linkages, as opposed to the single-node degrees, and can be applied to any real-world network -- in particular, those where node-node degree correlations might be important.
Minor typos corrected

Keywords

Statistical Mechanics, Disordered Systems and Neural Networks, Physics and Society

Citation

URI

http://salesiana.dossiersoluciones.com/handle/123456789/114805

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cond-mat/0608733
 http://arxiv.org/abs/cond-mat/0608733

Collections

arXiv