  We study the propagation of information in social networks. To do so, we
focus on a cascade model where nodes are infected with {probability $p_1$ after
their first contact with the information and with probability $p_2$ at all
subsequent contacts.} The diffusion starts from one random node and leads to a
cascade of infection. It is shown that first and {subsequent} trials play
different roles in the propagation and that the size of the cascade depends in
a non-trivial way on $p_1$, $p_2$ and on the network structure. Second trials
are shown to amplify the propagation in dense parts of the network while first
trials are {dominant for the exploration of} new parts of the network and
launching new seeds of infection.
