  Densification and sparsification of temporal networks are attributed to two
fundamental mechanisms: a change in the population in the system and/or a
change in the chances that nodes in the system are connected. In theory, each
of these mechanisms generates a distinctive type of densification scaling, but
in reality both types are generally mixed. Here, we develop a Bayesian
statistical method to identify the extent to which each of these mechanisms is
at play at a given point in time, taking the mixed densification scaling as
input. We apply the method to networks of face-to-face interactions of
individuals and reveal that the main mechanism that causes densification and
sparsification occasionally switches, the frequency of which depending on the
social context. The proposed method uncovers an inherent regime-switching
property of network dynamics, which will provide a new insight into the
mechanics behind evolving social interactions.
