  This paper examines the interplay of opinion exchange dynamics and
communication network formation. An opinion formation procedure is introduced
which is based on an abstract representation of opinions as $k$--dimensional
bit--strings. Individuals interact if the difference in the opinion strings is
below a defined similarity threshold $d_I$. Depending on $d_I$, different
behaviour of the population is observed: low values result in a state of highly
fragmented opinions and higher values yield consensus. The first contribution
of this research is to identify the values of parameters $d_I$ and $k$, such
that the transition between fragmented opinions and homogeneity takes place.
Then, we look at this transition from two perspectives: first by studying the
group size distribution and second by analysing the communication network that
is formed by the interactions that take place during the simulation. The
emerging networks are classified by statistical means and we find that
non--trivial social structures emerge from simple rules for individual
communication. Generating networks allows to compare model outcomes with
real--world communication patterns.
