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- W3028236585 abstract "Mixture models are probabilistic models aimed at uncovering and representing latent subgroups within a population. In the realm of network data analysis, the latent subgroups of nodes are typically identified by their connectivity behaviour, with nodes behaving similarly belonging to the same community. In this context, mixture modelling is pursued through stochastic blockmodelling. We consider stochastic blockmodels and some of their variants and extensions from a mixture modelling perspective. We also survey some of the main classes of estimation methods available, and propose an alternative approach. In addition to the discussion of inferential properties and estimating procedures, we focus on the application of the models to several real-world network datasets, showcasing the advantages and pitfalls of different approaches." @default.
- W3028236585 created "2020-05-29" @default.
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- W3028236585 date "2020-05-19" @default.
- W3028236585 modified "2023-09-27" @default.
- W3028236585 title "Mixture Models and Networks -- Overview of Stochastic Blockmodelling" @default.
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