Nonparametric Bayesian models of hierarchical structure in complex networks
This work addresses the need for hierarchical modeling in complex relational data, like brain connectivity analysis, but appears incremental as it builds on existing Bayesian and non-parametric approaches.
The authors tackled the problem of modeling hierarchical structure in complex networks, such as human brain connectivity, by proposing two non-parametric Bayesian hierarchical network models based on Gibbs fragmentation tree priors, and demonstrated their ability to capture nested patterns in simulations and achieve predictive performance on par with state-of-the-art methods on real networks.
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.