SELF-SIMILARITY IN COMPLEX NETWORKS: FROM THE VIEW OF THE HUB REPULSION-Reference-Cited by-同舟云学术

SELF-SIMILARITY IN COMPLEX NETWORKS: FROM THE VIEW OF THE HUB REPULSION

Published:2013-10-24 Issue:28 Volume:27 Page:1350201
ISSN:0217-9849
Container-title:Modern Physics Letters B
language:en
Short-container-title:Mod. Phys. Lett. B

Author:

ZHANG HAIXIN¹,LAN XIN¹,WEI DAIJUN¹,MAHADEVAN SANKARAN²,DENG YONG¹²

Affiliation:

1. School of Computer and Information Science, Southwest University, Chongqing 400715, China

2. School of Engineering, Vanderbilt University, Nashville, TN 37235, USA

Abstract

Complex networks are widely used to model the structure of many complex systems in nature and society. Recently, fractal and self-similarity of complex networks have attracted much attention. It is observed that hub repulsion is the key principle that leads to the fractal structure of networks. Based on the principle of hub repulsion, the metric in complex networks is redefined and a new method to calculate the fractal dimension of complex networks is proposed in this paper. Some real complex networks are investigated and the results are illustrated to show the self-similarity of complex networks.

Publisher

World Scientific Pub Co Pte Lt

Subject

Condensed Matter Physics,Statistical and Nonlinear Physics

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0217984913502011

Reference29 articles.

1. ANALYSIS OF A COMPLEX NETWORK OF PHYSICS CONCEPTS

2. Identifying influential nodes in weighted networks based on evidence theory

3. DETECTING CHAOS TIME SERIES VIA COMPLEX NETWORK FEATURE

4. EFFICIENCY DYNAMICS ON SCALE-FREE NETWORKS WITH COMMUNITIES

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Feature fusion technology based on serum FTIR spectra combined with chaos theory in the disease auxiliary diagnosis;Applied Soft Computing;2024-09

2. Fractal Analysis of Complex Networks Based on the Resistance Distance Box-Covering Algorithm;2023 8th International Conference on Computational Intelligence and Applications (ICCIA);2023-06-23

3. The self-similarity of complex networks: From the view of degree–degree distance;Modern Physics Letters B;2021-06-12

4. Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs;Atmospheric Pollution Research;2021-01

5. Mental Fatigue Has Great Impact on the Fractal Dimension of Brain Functional Network;Neural Plasticity;2020-11-12