  The classical random graph model $G(n,\lambda/n)$ satisfies a `duality
principle', in that removing the giant component from a supercritical instance
of the model leaves (essentially) a subcritical instance. Such principles have
been proved for various models; they are useful since it is often much easier
to study the subcritical model than to directly study small components in the
supercritical model. Here we prove a duality principle of this type for a very
general class of random graphs with independence between the edges, defined by
convergence of the matrices of edge probabilities in the cut metric.
