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

TL;DR

This paper introduces a penalized inference method for stochastic block models to analyze community structures in the Italian Parliament's bill cosponsorship networks, accounting for many political groups.

Contribution

It extends stochastic block models with a penalized likelihood approach to handle numerous parameters and infer sparse collaboration patterns among political parties.

Findings

Identified collaboration patterns between political groups.

Developed a sparse reduced graph of party collaborations.

Enhanced community detection in legislative networks.

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.