Scaling up real networks by geometric branching growth

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 21; pp. 1 - 10
• Main Authors: Zheng, Muhua, García-Pérez, Guillermo, Boguñá, Marián, Serrano, M. Ángeles
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 25-05-2021
• Subjects: Clustering; Community structure; Evolution; Growth models; Physical Sciences; Self-similarity; Social Sciences; complex network; self-similarity; network evolution; geometric renormalization; geometric branching growth
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2018994118

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.