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A penalized inference approach to stochastic block modelling of community structure in the Italian Parliament

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  • Mirko Signorelli
  • Ernst C. Wit

Abstract

We analyse bill cosponsorship networks in the Italian Chamber of Deputies. In comparison with other parliaments, a distinguishing feature of the Chamber is the large number of political groups. Our analysis aims to infer the pattern of collaborations between these groups from data on bill cosponsorships. We propose an extension of stochastic block models for edge‐valued graphs and derive measures of group productivity and of collaboration between political parties. As the model proposed encloses a large number of parameters, we pursue a penalized likelihood approach that enables us to infer a sparse reduced graph displaying collaborations between political parties.

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  • Mirko Signorelli & Ernst C. Wit, 2018. "A penalized inference approach to stochastic block modelling of community structure in the Italian Parliament," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(2), pages 355-369, February.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:2:p:355-369
    DOI: 10.1111/rssc.12234
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    Cited by:

    1. Mirko Signorelli & Luisa Cutillo, 2022. "On community structure validation in real networks," Computational Statistics, Springer, vol. 37(3), pages 1165-1183, July.

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