  To account for strong aging characteristics of citation networks, we modify
Google's PageRank algorithm by initially distributing random surfers
exponentially with age, in favor of more recent publications. The output of
this algorithm, which we call CiteRank, is interpreted as approximate traffic
to individual publications in a simple model of how researchers find new
information. We develop an analytical understanding of traffic flow in terms of
an RPA-like model and optimize parameters of our algorithm to achieve the best
performance. The results are compared for two rather different citation
networks: all American Physical Society publications and the set of high-energy
physics theory (hep-th) preprints. Despite major differences between these two
networks, we find that their optimal parameters for the CiteRank algorithm are
remarkably similar.
