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  Information Dynamics in Evolving Networks Based on the Birth-Death Process: Random Drift and Natural Selection Perspective

Feng, M., Zeng, Z., Li, Q., Perc, M., Kurths, J. (2024): Information Dynamics in Evolving Networks Based on the Birth-Death Process: Random Drift and Natural Selection Perspective. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54, 8, 5123-5136.
https://doi.org/10.1109/TSMC.2024.3389095

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Feng, Minyu1, Autor
Zeng, Ziyan1, Autor
Li, Qin1, Autor
Perc, Matjaž1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Dynamic processes in complex network are crucial for better understanding collective behavior in human societies, biological systems, and the Internet. In this article, we first focus on the continuous Markov-based modeling of evolving networks with the birth-death of individuals. A new individual arrives at the group by the Poisson process, while new links are established in the network through either uniform connection or preferential attachment. Moreover, an existing individual has a limited lifespan before leaving the network. We determine stationary topological properties of these networks, including their size and mean degree. To address the effect of the birth-death evolution, we further study the information dynamics in the proposed network model from the random drift and natural selection perspective, based on assumptions of total-stochastic and fitness-driven evolution, respectively. In simulations, we analyze the fixation probability of individual information and find that means of new connections affect the random drift process but do not affect the natural selection process.

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Sprache(n): eng - Englisch
 Datum: 2024-05-072024-08-01
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/TSMC.2024.3389095
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
MDB-ID: No data to archive
 Art des Abschluß: -

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Titel: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 54 (8) Artikelnummer: - Start- / Endseite: 5123 - 5136 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-systems-man-cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)