  Network science provides an indispensable theoretical framework for studying
the structure and function of real complex systems. Different network models
are often used for finding the rules that govern their evolution, whereby the
correct choice of model details is crucial for obtaining relevant insights. We
here study how the structure of networks generated with the aging nodes model
depends on the properties of the growth signal. We use different fluctuating
signals and compare structural dissimilarities of the networks with those
obtained with a constant growth signal. We show that networks with power-law
degree distributions, which are obtained with time-varying growth signals, are
correlated and clustered, while networks obtained with a constant growth signal
are not. Indeed, the properties of the growth signal significantly determine
the topology of the obtained networks and thus ought to be considered
prominently in models of complex systems.
