Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  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

Item is

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Li, Yuhan1, Autor
Feng, Minyu1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: 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.

Details

einblenden:
ausblenden:
Sprache(n): eng - Englisch
 Datum: 2023-05-012023-09
 Publikationsstatus: Final veröffentlicht
 Seiten: 12
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: 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
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 53 (9) Artikelnummer: - Start- / Endseite: 5369 - 5380 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-systems-man-cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)