The small-world effect of fractal networks modeled on 'dust-like' cubes

Cheng Zeng; Yuke Huang; Yumei Xue

doi:10.1142/S0217984922501585

The small-world effect of fractal networks modeled on 'dust-like' cubes

Cheng Zeng
, Yuke Huang^*
, Yumei Xue

^*Corresponding author for this work

School of Mathematical Sciences

Shandong Technology and Business University
Beijing University of Posts and Telecommunications

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, the sequence of evolving networks is generated from some 'dust-like' cubes by applying the encoding methods in fractal and symbolic dynamical systems. Based on the self-similar structures of fractals, we study the mean clustering coefficient, the mean geodesic distance and the mean Fermat distance. The relevant results show the small-world effect of our evolving networks.

Original language	English
Article number	2250158
Journal	Modern Physics Letters B
Volume	36
Issue number	28-29
DOIs	https://doi.org/10.1142/S0217984922501585
State	Published - 10 Oct 2022

Keywords

clustering coefficient
Dust-like fractal
Fermat distance
geodesic distance
self-similarity

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10.1142/S0217984922501585

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title = "The small-world effect of fractal networks modeled on 'dust-like' cubes",

abstract = "In this paper, the sequence of evolving networks is generated from some 'dust-like' cubes by applying the encoding methods in fractal and symbolic dynamical systems. Based on the self-similar structures of fractals, we study the mean clustering coefficient, the mean geodesic distance and the mean Fermat distance. The relevant results show the small-world effect of our evolving networks.",

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AU - Zeng, Cheng

AU - Huang, Yuke

AU - Xue, Yumei

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N2 - In this paper, the sequence of evolving networks is generated from some 'dust-like' cubes by applying the encoding methods in fractal and symbolic dynamical systems. Based on the self-similar structures of fractals, we study the mean clustering coefficient, the mean geodesic distance and the mean Fermat distance. The relevant results show the small-world effect of our evolving networks.

AB - In this paper, the sequence of evolving networks is generated from some 'dust-like' cubes by applying the encoding methods in fractal and symbolic dynamical systems. Based on the self-similar structures of fractals, we study the mean clustering coefficient, the mean geodesic distance and the mean Fermat distance. The relevant results show the small-world effect of our evolving networks.

KW - clustering coefficient

KW - Dust-like fractal

KW - Fermat distance

KW - geodesic distance

KW - self-similarity

UR - https://www.scopus.com/pages/publications/85144979657

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DO - 10.1142/S0217984922501585

M3 - 文章

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JO - Modern Physics Letters B

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The small-world effect of fractal networks modeled on 'dust-like' cubes

Abstract

Keywords

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