  When modeling a social dynamics with an agent-oriented approach, researchers
have to describe the structure of interactions within the population. Given the
intractability of extensive network collecting, they rely on random network
generators that are supposed to explore the space of plausible networks. We
first identify the needs of modelers, including placing heterogeneous agents on
the network given their attributes and differentiating the various types of
social links that lead to different interactions. We point out the existence of
data in the form of scattered statistics and qualitative observations, that
should be used to parameter the generator. We propose a new approach peculiar
to agent-based modeling, in which we will generate social links from
individuals' observed attributes, and return them as a multiplex network.
Interdependencies between socioeconomic attributes, and generative rules, are
encoded as Bayesian networks. A methodology guides modelers through the
formalization of these parameters. This approach is illustrated by describing
the structure of interactions that supports diffusion of contraceptive
solutions in rural Kenya.
