Nonparametric Bayesian models of hierarchical structure in complex networks

Mikkel N. Schmidt, Tue Herlau, Morten Mørup

AbstractAnalyzing 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.
TypeTechnical report
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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