  We demonstrate how to generalize two of the most well-known random graph
models, the classic random graph, and random graphs with a given degree
distribution, by the introduction of hidden variables in the form of extra
degrees of freedom, color, applied to vertices or stubs (half-edges). The color
is assumed unobservable, but is allowed to affect edge probabilities. This
serves as a convenient method to define very general classes of models within a
common unifying formalism, and allowing for a non-trivial edge correlation
structure.
