  We focus at the interface between multiscale computations, bifurcation theory
and social networks. In particular we address how the Equation-Free approach, a
recently developed computational framework, can be exploited to systematically
extract coarse-grained, emergent dynamical information by bridging detailed,
agent-based models of social interactions on networks, with macroscopic,
systems-level, continuum numerical analysis tools. For our illustrations we use
a simple dynamic agent-based model describing the propagation of information
between individuals interacting under mimesis in a social network with private
and public information. We describe the rules governing the evolution of the
agents emotional state dynamics and discover, through simulation, multiple
stable stationary states as a function of the network topology. Using the
Equation-Free approach we track the dependence of these stationary solutions on
network parameters and quantify their stability in the form of coarse-grained
bifurcation diagrams.
