Non-parametric Bayesian modeling of complex networks

Abstract

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.

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• Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks
• Bayesian Learning of Graph Substructures
• Bayesian Nonparametric Space Partitions: A Survey
• Combining Random Walks and Nonparametric Bayesian Topic Model for Community Detection
• A Bayesian Nonparametric Stochastic Block Model for Directed Acyclic Graphs
• Smoothing Graphons for Modelling Exchangeable Relational Data