  We investigate various aspects of the statistics of leaders in growing
network models defined by stochastic attachment rules. The leader is the node
with highest degree at a given time (or the node which reached that degree
first if there are co-leaders). This comprehensive study includes the full
distribution of the degree of the leader, its identity, the number of
co-leaders, as well as several observables characterizing the whole history of
lead changes: number of lead changes, number of distinct leaders, lead
persistence probability. We successively consider the following network models:
uniform attachment, linear attachment (the Barabasi-Albert model), and
generalized preferential attachment with initial attractiveness.
