A Penalized Inference Approach to Stochastic Block Modelling of Community Structure in the Italian Parliament. Issue 2 (27th July 2017)

• Record Type: Journal Article
• Authors: Signorelli, Mirko; Wit, Ernst C.
• Is Part Of: Journal of the Royal Statistical Society. Volume 67:Issue 2(2018:Mar.)
• Page Start: 355
• Page End: 369
• Publication Date: 2017-07-27
• Subjects: Adaptive lasso -- Bill cosponsorship -- Community structure -- Network -- Penalized likelihood -- Stochastic block model; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12234
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: 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.