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Various factors are believed to govern the selection of references incitation networks, but a precise, quantitative determination of theirimportance has remained elusive. In this paper, we show that three factors canaccount for the referencing pattern of citation networks for two topics, namely"graphenes" and "complex networks", thus allowing one to reproduce thetopological features of the networks built with papers being the nodes and theedges established by citations. The most relevant factor was contentsimilarity, while the other two - in-degree (i.e. citation counts) and {age ofpublication} had varying importance depending on the topic studied. Thisdependence indicates that additional factors could play a role. Indeed, byintuition one should expect the reputation (or visibility) of authors and/orinstitutions to affect the referencing pattern, and this is only indirectlyconsidered via the in-degree that should correlate with such reputation.Because information on reputation is not readily available, we simulated itseffect on artificial citation networks considering two communities withdistinct fitness (visibility) parameters. One community was assumed to havetwice the fitness value of the other, which amounts to a double probability fora paper being cited. While the h-index for authors in the community with largerfitness evolved with time with slightly higher values than for the controlnetwork (no fitness considered), a drastic effect was noted for the communitywith smaller fitness.

Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index