  Machine learning systems typically apply the same model to both easy and
tough cases. This is in stark contrast with humans, who tend to evoke either
fast (instinctive) or slow (analytical) thinking depending on the problem
difficulty, a property called the dual-process theory of mind. We present
FLOWGEN, a graph-generation model inspired by the dual-process theory of mind
that generates large graphs incrementally. Depending on the difficulty of
completing the graph at the current step, graph generation is routed to either
a fast (weaker) or a slow (stronger) model. These modules have identical
architectures, but vary in the number of parameters and consequently differ in
generative power. Experiments on real-world graphs show that ours can
successfully generate graphs similar to those generated by a single large
model, while being up to 2x faster.
