  We investigate the problem of learning to generate complex networks from
data. Specifically, we consider whether deep belief networks, dependency
networks, and members of the exponential random graph family can learn to
generate networks whose complex behavior is consistent with a set of input
examples. We find that the deep model is able to capture the complex behavior
of small networks, but that no model is able capture this behavior for networks
with more than a handful of nodes.
