Time-invariant degree growth in preferential attachment network models.

Sun, Jun; Medo, Matúš; Staab, Steffen (2020). Time-invariant degree growth in preferential attachment network models. Physical review. E - statistical, nonlinear, and soft matter physics, 101(2-1), 022309. American Physical Society 10.1103/PhysRevE.101.022309

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Preferential attachment drives the evolution of many complex networks. Its analytical studies mostly consider the simplest case of a network that grows uniformly in time despite the accelerating growth of many real networks. Motivated by the observation that the average degree growth of nodes is time invariant in empirical network data, we study the degree dynamics in the relevant class of network models where preferential attachment is combined with heterogeneous node fitness and aging. We propose an analytical framework based on the time invariance of the studied systems and show that it is self-consistent only for two special network growth forms: the uniform and the exponential network growth. Conversely, the breaking of such time invariance explains the winner-takes-all effect in some model settings, revealing the connection between the Bose-Einstein condensation in the Bianconi-Barabási model and similar gelation in superlinear preferential attachment. Aging is necessary to reproduce realistic node degree growth curves and can prevent the winner-takes-all effect under weak conditions. Our results are verified by extensive numerical simulations.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology

UniBE Contributor:

Medo, Matúš

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1539-3755

Publisher:

American Physical Society

Language:

English

Submitter:

Beatrice Scheidegger

Date Deposited:

21 Apr 2020 10:53

Last Modified:

26 Apr 2020 02:47

Publisher DOI:

10.1103/PhysRevE.101.022309

PubMed ID:

32168595

BORIS DOI:

10.7892/boris.142433

URI:

https://boris.unibe.ch/id/eprint/142433

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