...
首页> 外文期刊>Journal of the royal statistical society >A penalized inference approach to stochastic block modelling of community structure in the Italian Parliament
【24h】

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

机译:意大利议会社区结构的随机块建模的一种惩罚性推理方法

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

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.
机译:摘要。我们分析了意大利众议院的法案共同赞助网络。与其他议会相比,该会议厅的显着特征是政治团体众多。我们的分析旨在从票据共同赞助数据中推断出这些群体之间的合作模式。我们提议对边值图进行随机块模型的扩展,并得出团体生产率和政党之间合作的度量。由于所提出的模型包含大量参数,因此我们采用了一种惩罚似然法,该方法使我们能够推断出显示政党之间合作的稀疏简化图。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号