  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.
