  In real world domains, most graphs naturally exhibit a hierarchical
structure. However, data-driven graph generation is yet to effectively capture
such structures. To address this, we propose a novel approach that recursively
generates community structures at multiple resolutions, with the generated
structures conforming to training data distribution at each level of the
hierarchy. The graphs generation is designed as a sequence of coarse-to-fine
generative models allowing for parallel generation of all sub-structures,
resulting in a high degree of scalability. Our method demonstrates generative
performance improvement on multiple graph datasets.
