Scaling up real networks by geometric branching growth

Muhua Zheng, Guillermo García-Pérez, Marián Boguñá and M. Ángeles Serrano ()
Additional contact information
Muhua Zheng: School of Physics and Electronic Engineering, Jiangsu University, Zhenjiang 212013, China; Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems, Universitat de Barcelona, 08028 Barcelona, Spain
Guillermo García-Pérez: Quantum Technology Finland Centre of Excellence, Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turun Yliopisto, Finland; Complex Systems Research Group, Department of Mathematics and Statistics, University of Turku, FI-20014 Turun Yliopisto, Finland; Algorithmiq Ltd, 20100 Turku, Finland
Marián Boguñá: Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems, Universitat de Barcelona, 08028 Barcelona, Spain
M. Ángeles Serrano: Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems, Universitat de Barcelona, 08028 Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), 08010 Barcelona, Spain

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 21, e2018994118

Abstract: Real networks often grow through the sequential addition of new nodes that connect to older ones in the graph. However, many real systems evolve through the branching of fundamental units, whether those be scientific fields, countries, or species. Here, we provide empirical evidence for self-similar growth of network structure in the evolution of real systems—the journal-citation network and the world trade web—and present the geometric branching growth model, which predicts this evolution and explains the symmetries observed. The model produces multiscale unfolding of a network in a sequence of scaled-up replicas preserving network features, including clustering and community structure, at all scales. Practical applications in real instances include the tuning of network size for best response to external influence and finite-size scaling to assess critical behavior under random link failures.

Keywords: complex network; self-similarity; network evolution; geometric branching growth; geometric renormalization (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.pnas.org/content/118/21/e2018994118.full (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:118:y:2021:p:e2018994118

Access Statistics for this article

More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences
Bibliographic data for series maintained by PNAS Product Team ().