  Individual nodes in evolving real-world networks typically experience growth
and decay --- that is, the popularity and influence of individuals peaks and
then fades. In this paper, we study this phenomenon via an intrinsic nodal
fitness function and an intuitive aging mechanism. Each node of the network is
endowed with a fitness which represents its activity. All the nodes have two
discrete stages: active and inactive. The evolution of the network combines the
addition of new active nodes randomly connected to existing active ones and the
deactivation of old active nodes with possibility inversely proportional to
their fitnesses. We obtain a structured exponential network when the fitness
distribution of the individuals is homogeneous and a structured scale-free
network with heterogeneous fitness distributions. Furthermore, we recover two
universal scaling laws of the clustering coefficient for both cases, $C(k) \sim
k^{-1}$ and $C \sim n^{-1}$, where $k$ and $n$ refer to the node degree and the
number of active individuals, respectively. These results offer a new simple
description of the growth and aging of networks where intrinsic features of
individual nodes drive their popularity, and hence degree.
