Classification and estimation in the stochastic blockmodel based on the empirical degrees

DOI10.1214/12-EJS753zbMath1295.62065arXiv1110.6517OpenAlexW2963723689MaRDI QIDQ1950919

Antoine Channarond, Jean-Jacques Daudin, Stephane Robin

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1110.6517

zbMATH Keywords

estimationmodel selectionclusteringstochastic blockmodelunsupervised classification

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (11)

Bayesian community detection ⋮ Uniform estimation in stochastic block models is slow ⋮ Variational Bayes model averaging for graphon functions and motif frequencies inference in \(W\)-graph models ⋮ Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels ⋮ Posterior contraction rates for stochastic block models ⋮ Review on statistical methods for gene network reconstruction using expression data ⋮ Pseudo-likelihood methods for community detection in large sparse networks ⋮ Weighted stochastic block model ⋮ Consistency of spectral clustering in stochastic block models ⋮ Convergence of the groups posterior distribution in latent or stochastic block models ⋮ Inferring gene-gene interactions and functional modules using sparse canonical correlation analysis

Uses Software

• Mixnet

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Spectral clustering and the high-dimensional stochastic blockmodel
• The method of moments and degree distributions for network models
• Estimation and prediction for stochastic blockmodels for graphs with latent block structure
• Consistency of maximum-likelihood and variational estimators in the stochastic block model
• A nonparametric view of network models and Newman–Girvan and other modularities
• Statistical mechanics of complex networks
• Graphical Models, Exponential Families, and Variational Inference
• An Exponential Family of Probability Distributions for Directed Graphs
• Estimation and Prediction for Stochastic Blockstructures
• Community structure in social and biological networks
• The phase transition in inhomogeneous random graphs
• Random Graphs

This page was built for publication: Classification and estimation in the stochastic blockmodel based on the empirical degrees