A1 Refereed original research article in a scientific journal
Scaling up real networks by geometric branching growth
Authors: Zheng Muhua, García-Pérez Guillermo, Boguñá Marián, Serrano M. Ángeles
Publisher: National Academy of Sciences
Publication year: 2021
Journal: Proceedings of the National Academy of Sciences of the United States of America
Journal name in source: Proceedings of the National Academy of Sciences of the United States of America
Journal acronym: Proc Natl Acad Sci U S A
Article number: e2018994118
Volume: 118
Issue: 21
ISSN: 0027-8424
eISSN: 1091-6490
DOI: https://doi.org/10.1073/pnas.2018994118
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/58942303
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.
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