Two types of densification scaling in the evolution of temporal networks

Teruyoshi Kobayashi (1, 2) , Mathieu Génois (3, 4, 5)

1 Department of Economics, Kobe University
2 Center for Computational Social Science, Kobe University
3 CPT - Centre de Physique Théorique - UMR 7332
4 AMU - Aix Marseille Université
5 CPT - E5 Physique statistique et systèmes complexes

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.

Domaines: Physique et Société [physics.soc-ph]; Analyse de données, Statistiques et Probabilités [physics.data-an]; Santé publique et épidémiologie; Sociologie; Adaptation et Systèmes auto-organisés [nlin.AO]

Teruyoshi Kobayashi, Mathieu Génois. Two types of densification scaling in the evolution of temporal networks. Physical Review E , 2020, 102 (5), pp.052302. ⟨10.1103/PhysRevE.102.052302⟩. ⟨hal-03094946⟩