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

Zeng, Ziyan; Feng, Minyu; Kurths, Jürgen

doi:10.1016/j.apm.2023.05.034

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

Authors

Zeng, Ziyan
External Organizations;

Feng, Minyu
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/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

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引用

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. doi:10.1016/j.apm.2023.05.034.

引用: https://publications.pik-potsdam.de/pubman/item/item_29115

要旨

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.