  Inspired by the generation power of generative adversarial networks (GANs) in
image domains, we introduce a novel hierarchical architecture for learning
characteristic topological features from a single arbitrary input graph via
GANs. The hierarchical architecture consisting of multiple GANs preserves both
local and global topological features and automatically partitions the input
graph into representative stages for feature learning. The stages facilitate
reconstruction and can be used as indicators of the importance of the
associated topological structures. Experiments show that our method produces
subgraphs retaining a wide range of topological features, even in early
reconstruction stages (unlike a single GAN, which cannot easily identify such
features, let alone reconstruct the original graph). This paper is firstline
research on combining the use of GANs and graph topological analysis.
