A dynamic stochastic blockmodel for interaction lengths

DOI10.48550/arXiv.1901.09828zbMath1474.62243arXiv1901.09828OpenAlexW3036630459MaRDI QIDQ143078

Michael Fop, Riccardo Rastelli, Michael Fop

Publication date: 28 January 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1901.09828

zbMATH Keywords

stochastic block modelintegrated completed likelihoodvariational inferencesocial network analysisinteraction lengths

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Social networks; opinion dynamics (91D30)

Related Items (1)

expSBM

Uses Software

• R

Cites Work

• Unnamed Item
• Unnamed Item
• The stochastic topic block model for the clustering of vertices in networks with textual edges
• Detecting communities and their evolutions in dynamic social networks -- a Bayesian approach
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• On the convergence properties of the EM algorithm
• Computational aspects of fitting mixture models via the expectation-maximization algorithm
• Consistency of maximum-likelihood and variational estimators in the stochastic block model
• Discrete temporal models of social networks
• Improved initialisation of model-based clustering using Gaussian hierarchical partitions
• Finite mixture and Markov switching models.
• Mixed membership stochastic blockmodels
• An Exponential Family of Probability Distributions for Directed Graphs
• Latent Space Approaches to Social Network Analysis
• Dealing With Label Switching in Mixture Models
• A semiparametric extension of the stochastic block model for longitudinal networks
• New Consistent and Asymptotically Normal Parameter Estimates for Random-Graph Mixture Models
• Stochastic Blockmodels for Directed Graphs
• Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood
• Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model
• Latent Space Models for Dynamic Networks
• EM for mixtures

This page was built for publication: A dynamic stochastic blockmodel for interaction lengths