  Adaptive networks model social, physical, technical, or biological systems as
attributed graphs evolving at the level of both their topology and data. They
are naturally described by graph transformation, but the majority of authors
take an approach inspired by the physical sciences, combining an informal
description of the operations with programmed simulations, and systems of ODEs
as the only abstract mathematical description. We show that we can capture a
range of social network models, the so-called voter models, as stochastic
attributed graph transformation systems, demonstrate the benefits of this
representation and establish its relation to the non-standard probabilistic
view adopted in the literature. We use the theory and tools of graph
transformation to analyze and simulate the models and propose a new variant of
a standard stochastic simulation algorithm to recreate the results observed.
