Nonparametric Bayes Modeling of Populations of Networks

Journal Article (Journal Article)

Replicated network data are increasingly available in many research fields. For example, in connectomic applications, interconnections among brain regions are collected for each patient under study, motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a single network are not designed specifically for inference on the entire probability mass function of a network-valued random variable and therefore lack flexibility in characterizing the distribution of relevant topological structures. We propose a flexible Bayesian nonparametric approach for modeling the population distribution of network-valued data. The joint distribution of the edges is defined via a mixture model that reduces dimensionality and efficiently incorporates network information within each mixture component by leveraging latent space representations. The formulation leads to an efficient Gibbs sampler and provides simple and coherent strategies for inference and goodness-of-fit assessments. We provide theoretical results on the flexibility of our model and illustrate improved performance—compared to state-of-the-art models—in simulations and application to human brain networks. Supplementary materials for this article are available online.

Full Text

Duke Authors

Cited Authors

  • Durante, D; Dunson, DB; Vogelstein, JT

Published Date

  • October 2, 2017

Published In

Volume / Issue

  • 112 / 520

Start / End Page

  • 1516 - 1530

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.2016.1219260

Citation Source

  • Scopus