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  Temporal network modeling with online and hidden vertices based on the birth and death process

Zeng, Z., Feng, M., Kurths, J. (2023): Temporal network modeling with online and hidden vertices based on the birth and death process. - Applied Mathematical Modelling, 122, 151-166.
https://doi.org/10.1016/j.apm.2023.05.034

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 Creators:
Zeng, Ziyan1, 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: Complex networks have played an important role in describing real complex systems since the end of the last century. Recently, research on real-world data sets reports intermittent interaction among social individuals. In this paper, we pay attention to this typical phenomenon of intermittent interaction by considering the state transition of network vertices between online and hidden based on the birth and death process. By continuous-time Markov theory, we show that both the number of each vertex’s online neighbors and the online network size are stable and follow the homogeneous probability distribution in a similar form, inducing similar statistics as well. In addition, all propositions are verified via simulations. Moreover, we also present the degree distributions based on small-world and scale-free networks and find some regular patterns by simulations. The application in fitting real networks is discussed.

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Language(s): eng - English
 Dates: 2023-06-062023-10-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.apm.2023.05.034
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
Model / method: Machine Learning
 Degree: -

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Title: Applied Mathematical Modelling
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 122 Sequence Number: - Start / End Page: 151 - 166 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals34
Publisher: Elsevier