Weighted stochastic block model
DOI10.1007/s10260-021-00590-6zbMath1477.62153OpenAlexW3199189318MaRDI QIDQ2066716
Tin Lok James Ng, Thomas Brendan Murphy
Publication date: 14 January 2022
Published in: Statistical Methods and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10260-021-00590-6
consistencymodel selectionmaximum likelihood estimatorsvariational estimatorsweighted stochastic block model
Asymptotic properties of parametric estimators (62F12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Random graphs (graph-theoretic aspects) (05C80) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)
Cites Work
- Likelihood-based model selection for stochastic block models
- Bayesian degree-corrected stochastic blockmodels for community detection
- Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels
- Overlapping stochastic block models with application to the French political blogosphere
- A Bernstein-type inequality for suprema of random processes with applications to model selection in non-Gaussian regression
- Parameter identifiability in a class of random graph mixture models
- Spectral clustering and the high-dimensional stochastic blockmodel
- The method of moments and degree distributions for network models
- Detection of structurally homogeneous subsets in graphs
- Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models
- Strategies for online inference of model-based clustering in large and growing networks
- Uncovering latent structure in valued graphs: a variational approach
- Identifiability of parameters in latent structure models with many observed variables
- Estimation and prediction for stochastic blockmodels for graphs with latent block structure
- Consistency of maximum-likelihood and variational estimators in the stochastic block model
- Classification and estimation in the stochastic blockmodel based on the empirical degrees
- Model selection in overlapping stochastic block models
- Consistency and asymptotic normality of latent block model estimators
- Stochastic blockmodels with a growing number of classes
- A nonparametric view of network models and Newman–Girvan and other modularities
- Mixed membership stochastic blockmodels
- Community Detection and Stochastic Block Models
- New Consistent and Asymptotically Normal Parameter Estimates for Random-Graph Mixture Models
- Learning latent block structure in weighted networks
- Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood
- Computing Communities in Large Networks Using Random Walks
- Closed-Form Estimators for the Gamma Distribution Derived From Likelihood Equations
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