  This paper is first-line research expanding GANs into graph topology
analysis. By leveraging the hierarchical connectivity structure of a graph, we
have demonstrated that generative adversarial networks (GANs) can successfully
capture topological features of any arbitrary graph, and rank edge sets by
different stages according to their contribution to topology reconstruction.
Moreover, in addition to acting as an indicator of graph reconstruction, we
find that these stages can also preserve important topological features in a
graph.
