Learning to Generate Networks
This addresses network generation for researchers, but it is incremental as it shows limitations of existing methods without major breakthroughs.
The paper tackled the problem of generating complex networks from data using models like deep belief networks and exponential random graphs, finding that deep models captured behavior for small networks but all models failed for networks with more than a handful of nodes.
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.