  We propose a new preferential attachment-based network growth model in order
to explain two properties of growing networks: (1) the power-law growth of node
degrees and (2) the decay of node relevance. In preferential attachment models,
the ability of a node to acquire links is affected by its degree, its fitness,
as well as its relevance which typically decays over time. After a review of
existing models, we argue that they cannot explain the above-mentioned two
properties (1) and (2) at the same time. We have found that apart from being
empirically observed in many systems, the exponential growth of the network
size over time is the key to sustain the power-law growth of node degrees when
node relevance decays. We therefore make a clear distinction between the event
time and the physical time in our model, and show that under the assumption
that the relevance of a node decays with its age $\tau$, there exists an
analytical solution of the decay function $f_R$ with the form $f_R(\tau) =
\tau^{-1}$. Other properties of real networks such as power-law alike degree
distributions can still be preserved, as supported by our experiments. This
makes our model useful in explaining and analysing many real systems such as
citation networks.
