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  Evolving Network Modeling Driven by the Degree Increase and Decrease Mechanism

Li, Y., Feng, M., Kurths, J. (2023): Evolving Network Modeling Driven by the Degree Increase and Decrease Mechanism. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53, 9, 5369-5380.
https://doi.org/10.1109/TSMC.2023.3268372

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 Creators:
Li, Yuhan1, Author
Feng, Minyu1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Ever since the Barabási–Albert (BA) scale-free network has been proposed, network modeling has been studied intensively in light of the network growth and the preferential attachment (PA). However, numerous real systems are featured with a dynamic evolution including network reduction in addition to network growth. In this article, we propose a novel mechanism for evolving networks from the perspective of vertex degree. We construct a queueing system to describe the increase and decrease of vertex degree, which drives the network evolution. In our mechanism, the degree increase rate is regarded as a function positively correlated to the degree of a vertex, ensuring the PA in a new way. Degree distributions are investigated under two expressions of the degree increase rate, one of which manifests a “long tail”, and another one varies with different values of parameters. In simulations, we compare our theoretical distributions with simulation results and also apply them to real networks, which presents the validity and applicability of our model.

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Language(s): eng - English
 Dates: 2023-05-012023-09
 Publication Status: Finally published
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TSMC.2023.3268372
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Tipping Elements
Model / method: Game Theory
Model / method: Nonlinear Data Analysis
 Degree: -

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Title: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 53 (9) Sequence Number: - Start / End Page: 5369 - 5380 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-systems-man-cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)