  Analyzing and understanding the structure of complex relational data is
important in many applications including analysis of the connectivity in the
human brain. Such networks can have prominent patterns on different scales,
calling for a hierarchically structured model. We propose two non-parametric
Bayesian hierarchical network models based on Gibbs fragmentation tree priors,
and demonstrate their ability to capture nested patterns in simulated networks.
On real networks we demonstrate detection of hierarchical structure and show
predictive performance on par with the state of the art. We envision that our
methods can be employed in exploratory analysis of large scale complex networks
for example to model human brain connectivity.
