  Word-of-Mouth refers to the dynamics of interpersonal communication occurring
during the diffusion of innovations (novel practices, ideas or products).
According to field studies, word-of-mouth is made of both information seeking
and proactive communication: individuals first become aware of the existence of
an innovation, then start actively seeking out for the expert knowledge
required to evaluate the innovation; when they hold the expert knowledge, they
might start promoting it pro-actively. Successful diffusion of innovation
requires the individuals to hold both awareness and expert knowledge, so they
can evaluate the innovation and use it properly. A computational model
"USA/IPK" was recently proposed to study the role and impact of information
seeking on the dynamics of word-of-mouth. We propose here an analysis of the
impact of the network of interaction on the dynamics of this model. We compare
the dynamics of the model over networks generated with different algorithms
with the original dynamics. The results demonstrate the dynamics of the model
are similar across tested networks, with the noticeable exception of the
efficiency of the diffusion which varies between networks having similar
densities and sizes.
