Variational Inference for Stochastic Block Models From Sampled Data

DOI10.1080/01621459.2018.1562934zbMath1437.62072arXiv1707.04141OpenAlexW2757220467MaRDI QIDQ99254

Pierre Barbillon, Timothée Tabouy, Julien Chiquet, Timothée Tabouy, Pierre Barbillon, Julien Chiquet

Publication date: 11 April 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1707.04141

zbMATH Keywords

missing datastochastic block modelvariational inferencesampled network

Mathematics Subject Classification ID

Bayesian inference (62F15) Numerical optimization and variational techniques (65K10) Statistical block designs (62K10) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Missing data (62D10)

Related Items (7)

Hybrid maximum likelihood inference for stochastic block models ⋮ Consistency and asymptotic normality of stochastic block models estimators from sampled data ⋮ Outlier detection in networks with missing links ⋮ Maximum likelihood estimation of sparse networks with missing observations ⋮ missSBM ⋮ Learning from missing data with the binary latent block model ⋮ Estimation of dense stochastic block models visited by random walks

Uses Software

• R
• STRING

Cites Work

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