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  Heritable Deleting Strategies for Birth and Death Evolving Networks From a Queueing System Perspective

Feng, M., Li, Y., Chen, F., Kurths, J. (2022): Heritable Deleting Strategies for Birth and Death Evolving Networks From a Queueing System Perspective. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 10, 6662-6673.
https://doi.org/10.1109/TSMC.2022.3149596

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 Urheber:
Feng, Minyu 1, Autor
Li, Yuhan 1, Autor
Chen, Feng 1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Evolving networks have always been studied a lot featuring the dynamic properties of real-life networks. Studying the mechanism of the growth and death of a network is of great significance to network modeling. Identical to many models focused on the growing process, in this article, we study the decreasing process thoroughly. A novel evolving network model considering the growing and decreasing process is established based on the queueing system. Focused on the degreasing process, we originally investigate two strategies of vertex deleting that are the brutal strategy and the heritable strategy which characterizes the heritable behavior of ``dying'' vertices in real networks. On the basis of our model, stochastic properties of the proposed network are analyzed, e.g., the distribution and the expectation of the stationary scale of the network are theoretically obtained. In addition to that, degree distributions with different strategies are demonstrated in simulations, which manifests the power-low distribution. The reliability of the network is also studied by different attacks, sharing the same characteristic with the scale-free network.

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Sprache(n): eng - Englisch
 Datum: 2022-02-142022-10
 Publikationsstatus: Final veröffentlicht
 Seiten: 12
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/TSMC.2022.3149596
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Model / method: Game Theory
Working Group: Network- and machine-learning-based prediction of extreme events
 Art des Abschluß: -

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Titel: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 52 (10) Artikelnummer: - Start- / Endseite: 6662 - 6673 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-systems-man-cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)