  An analytical approach to network dynamics is used to show that when agents
copy their state randomly the network arrives to a stationary status in which
the distribution of states is independent of the agents degree. The effects of
network topology on the process are characterized introducing a quantity called
influence and studying its behavior for scale-free and random networks. We show
that for this model degree averaged means are constant in time regardless of
the number of states involved.
