A BAYESIAN APPROACH TO MODELING STOCHASTIC BLOCKSTRUCTURES WITH COVARIATES

DOI10.1080/00222500590889703zbMath1101.91067OpenAlexW1986592861MaRDI QIDQ4681844

Christian Tallberg

Publication date: 29 June 2005

Published in: The Journal of Mathematical Sociology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00222500590889703

zbMATH Keywords

Bayesian analysisGibbs samplingRandom graphsMultinomial probitBlock models

Mathematics Subject Classification ID

Social networks; opinion dynamics (91D30) Bayesian inference (62F15) Applications of graph theory (05C90) Random graphs (graph-theoretic aspects) (05C80)

Related Items (15)

Bayesian testing for exogenous partition structures in stochastic block models ⋮ Bayesian degree-corrected stochastic blockmodels for community detection ⋮ Edge Exchangeable Models for Interaction Networks ⋮ An alternative class of models to position social network groups in latent spaces ⋮ Triangular Concordance Learning of Networks ⋮ Mixture models and networks: The stochastic blockmodel ⋮ Hierarchical Network Models for Exchangeable Structured Interaction Processes ⋮ Modeling heterogeneous peer assortment effects using finite mixture exponential random graph models ⋮ Parameter identifiability in a class of random graph mixture models ⋮ Inferring sparse Gaussian graphical models with latent structure ⋮ Model-based clustering of large networks ⋮ Recovering Social Networks from Individual Attributes ⋮ Likelihood Inference for Large Scale Stochastic Blockmodels With Covariates Based on a Divide-and-Conquer Parallelizable Algorithm With Communication ⋮ Extended stochastic block models with application to criminal networks ⋮ Identifiability of parameters in latent structure models with many observed variables

Cites Work

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• Unnamed Item

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