2013/12/31 by Vincenzo Nicosia, Ginestra Bianconi, Vito Latora +2 · 80 citations
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Bioinformatics #Biological system #Biology #Class (philosophy) #Combinatorics #Complex Network Analysis Techniques #Computer science #Degree (music) #Homogeneous #Layer (electronics) #Materials science #Mathematics #Multiplex #Nanotechnology #Node (physics) #Nonlinear Dynamics and Pattern Formation #Nonlinear system #Opinion Dynamics and Social Influence #Physics #Quantum mechanics #Set (abstract data type) #Statistical physics #Topology (electrical circuits) #cond-mat.dis-nn #cond-mat.stat-mech #cs.SI #physics.soc-ph
paper · pdf · doi:10.1103/physreve.90.042807
published in Physical Review E 90(4), 042807 (American Physical Society) · 15 pages, 9 figures
arxiv created 2014/07/24 · openalex publication_date 2014/10/14 · arxiv updated 2014/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Different types of interactions coexist and coevolve to shape the structure and function of a multiplex network. We propose here a general class of growth models in which the various layers of a multiplex network coevolve through a set of nonlinear preferential attachment rules. We show, both numerically and analytically, that by tuning the level of nonlinearity these models allow us to reproduce either homogeneous or heterogeneous degree distributions, together with positive or negative degree correlations across layers. In particular, we derive the condition for the appearance of a condensed state in which one node in each layer attracts an extensive fraction of all the edges.