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Heterogeneity for the Stochastic Blockmodel and the Issue of Separation in Political Networks

Lefeber, F (2017) Heterogeneity for the Stochastic Blockmodel and the Issue of Separation in Political Networks. Master's Thesis / Essay, Science Education and Communication.

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The stochastic blockmodel is a useful tool for modeling networks in which group structures are present. However, this model assumes homogeneity among all individuals within the same group. In a recent study, Signorelli and Wit (2016) tried a stochastic blockmodel approach with added individual attributes of deputies, which allowed for heterogeneity. We explore a different approach in this study, by extending the stochastic blockmodel with individual effects, actually stepping away from its blockmodel property, keeping block interactions intact. We compare its results to the ones from the basic stochastic blockmodel. We compare two inferential procedures, one being the maximum likelihood estimation and another being penalized likelihood estimation with the adaptive lasso. We explain our methodology and test the models on some toy examples of networks. Then we use them on real data from the Finnish and Italian parliaments. During our analyses we encounter strengths and weaknesses of the various models, including a vulnerability to separation in the data.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Science Education and Communication
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:27
Last Modified: 15 Feb 2018 08:27

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