Degree distribution and scaling in the connecting-nearest-neighbors model

B Rudolf; M Markosova; M Cajagi; Peter Tino

doi:10.1103/PhysRevE.85.026114

Degree distribution and scaling in the connecting-nearest-neighbors model

B Rudolf, M Markosova, M Cajagi, Peter Tino

Computer Science

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9 Citations (Scopus)

Abstract

We present a detailed analysis of the connecting-nearest-neighbors model by Vazquez [Phys. Rev. E 67, 056104 ( 2003)]. We show that the degree distribution follows a power law, but the scaling exponent can vary with the parameter setting. Moreover, the correspondence of the growing version of the connecting-nearest-neighbors model to the particular random walk model and recursive search model is established.

Original language	English
Pages (from-to)	026114
Number of pages	1
Journal	Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)
Volume	85
Issue number	2
DOIs	https://doi.org/10.1103/PhysRevE.85.026114
Publication status	Published - 1 Feb 2012

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10.1103/PhysRevE.85.026114

Unified probabilistic modelleing of adaptive spatial temporal structures in the human brain
Tino, P. & Kourtzi, Z.
Biotechnology & Biological Sciences Research Council
1/10/10 → 30/03/14
Project: Research Councils

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title = "Degree distribution and scaling in the connecting-nearest-neighbors model",

abstract = "We present a detailed analysis of the connecting-nearest-neighbors model by Vazquez [Phys. Rev. E 67, 056104 ( 2003)]. We show that the degree distribution follows a power law, but the scaling exponent can vary with the parameter setting. Moreover, the correspondence of the growing version of the connecting-nearest-neighbors model to the particular random walk model and recursive search model is established.",

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Degree distribution and scaling in the connecting-nearest-neighbors model

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Unified probabilistic modelleing of adaptive spatial temporal structures in the human brain

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