Non-parametric Bayesian modeling of complex networks
This provides a tutorial for researchers in network analysis and machine learning, but it is incremental as it reviews and explains existing methods rather than presenting new results.
The paper tackles the problem of modeling complex networks by introducing non-parametric Bayesian methods, which allow flexible model specification and automatic inference of model complexity from data, using an infinite mixture model as an example to illustrate derivation, parameter inference via Markov chain Monte Carlo, and model evaluation.
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model as running example we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model's fit and predictive performance. We explain how advanced non-parametric models for complex networks can be derived and point out relevant literature.