  We propose a robust, scalable, integrated methodology for community detection
and community comparison in graphs. In our procedure, we first embed a graph
into an appropriate Euclidean space to obtain a low-dimensional representation,
and then cluster the vertices into communities. We next employ nonparametric
graph inference techniques to identify structural similarity among these
communities. These two steps are then applied recursively on the communities,
allowing us to detect more fine-grained structure. We describe a hierarchical
stochastic blockmodel---namely, a stochastic blockmodel with a natural
hierarchical structure---and establish conditions under which our algorithm
yields consistent estimates of model parameters and motifs, which we define to
be stochastically similar groups of subgraphs. Finally, we demonstrate the
effectiveness of our algorithm in both simulated and real data. Specifically,
we address the problem of locating similar subcommunities in a partially
reconstructed Drosophila connectome and in the social network Friendster.
