  We investigate how leaders emerge as a consequence of the competitive
dynamics between coupled papers in a model citation network. Every paper is
allocated an initial fitness depending on its intrinsic quality. Its fitness
then evolves dynamically as a consequence of the competition between itself and
all the other papers in the field. It picks up citations as a result of this
adaptive dynamics, becoming a leader if it has the highest citation count at a
given time. Extensive analytical and numerical investigations of this model
suggest the existence of a universal phase diagram, divided into regions of
weak and strong coupling. In the former, we find an `extended' and rather
structureless distribution of citation counts among many fit papers; leaders
are not necessarily those with the maximal fitness at any given time. By
contrast, the strong-coupling region is characterised by a strongly
hierarchical distribution of citation counts, that are `localised' among only a
few extremely fit papers, and exhibit strong history-to-history fluctuations,
as a result of the complex dynamics among papers in the tail of the fitness
distribution.
