We consider propagation of influence on a Configuration Model, where each
vertex can be influenced by any of its neighbours but in its turn, it can only
influence a random subset of its neighbours. Our (enhanced) model is described
by the total degree of the typical vertex, representing the total number of its
neighbours and the transmitter degree, representing the number of neighbours it
is able to influence. We give a condition involving the joint distribution of
these two degrees, which if satisfied would allow with high probability the
influence to reach a non-negligible fraction of the vertices, called a big
(influenced) component, provided that the source vertex is chosen from a set of
good pioneers. We show that asymptotically the big component is essentially the
same, regardless of the good pioneer we choose, and we explicitly evaluate the
asymptotic relative size of this component. Finally, under some additional
technical assumption we calculate the relative size of the set of good
pioneers. The main technical tool employed is the "fluid limit" analysis of the
joint exploration of the configuration model and the propagation of the
influence up to the time when a big influenced component is completed. This
method was introduced in Janson & Luczak (2008) to study the giant component of
the configuration model. Using this approach we study also a reverse dynamic,
which traces all the possible sources of influence of a given vertex, and which
by a new "duality" relation allows to characterise the set of good pioneers
