2020/05/31 by Teruyoshi Kobayashi, Mathieu Génois
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computer science #Dynamical systems theory #Evolutionary Game Theory and Cooperation #Mathematics #Opinion Dynamics and Social Influence #Physics #Population #Scaling #Statistical physics #cs.SI #physics.soc-ph
paper · pdf · doi:10.1103/physreve.102.052302
published as Phys. Rev. E 102, 052302 (2020) · 12 pages, 6 figures (plus 7 figures in SI)
arxiv created 2020/10/19 · openalex publication_date 2020/11/09 · arxiv updated 2020/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Many real-world social networks constantly change their global properties over time, such as the number of edges, size, and density. While temporal and local properties of social networks have been extensively studied, the origin of their dynamical nature is not yet well understood. Networks may grow or shrink if (a) the total population of nodes changes and/or (b) the chance of two nodes being connected varies over time. Here, we develop a method that allows us to classify the source of time-varying nature of temporal networks. In doing so, we first show empirical evidence that real-world dynamical systems could be categorized into two classes, the difference of which is characterized by the way the number of edges grows with the number of active nodes, i.e., densification scaling. We develop a dynamic hidden-variable model to formally characterize the two dynamical classes. The model is fitted to the empirical data to identify whether the origin of scaling comes from a changing population in the system or shifts in the connecting probabilities.