A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging

DOI10.1214/20-AOAS1339zbMath1446.62287arXiv1805.02292OpenAlexW3037394409MaRDI QIDQ2194484

Subhadeep Paul, Yuguo Chen

Publication date: 26 August 2020

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1805.02292

zbMATH Keywords

community detectionnonnegative matrix factorizationneuroimagingpopulation of networksrandom effects stochastic block model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Factorization of matrices (15A23) Image analysis in multivariate analysis (62H35)

Related Items

Testing for association in multiview network data ⋮ Community detection for multilayer weighted networks ⋮ Bayesian learning of graph substructures ⋮ Latent Space Model for Higher-Order Networks and Generalized Tensor Decomposition ⋮ Covariate-Assisted Community Detection in Multi-Layer Networks ⋮ Community detection with dependent connectivity ⋮ Community detection on mixture multilayer networks via regularized tensor decomposition ⋮ A latent space model for multilayer network data ⋮ Global and individualized community detection in inhomogeneous multilayer networks

Uses Software

• GenLouvain
• CONN
• BrainNet Viewer
• Brain Connectivity Toolbox

Cites Work

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