  We show that to explain the growth of the citation network by preferential
attachment (PA), one has to accept that individual nodes exhibit heterogeneous
fitness values that decay with time. While previous PA-based models assumed
either heterogeneity or decay in isolation, we propose a simple analytically
treatable model that combines these two factors. Depending on the input
assumptions, the resulting degree distribution shows an exponential, log-normal
or power-law decay, which makes the model an apt candidate for modeling a wide
range of real systems.
