A block model for node popularity in networks with community structure

• 成果类型: Article
• 署名作者: Sengupta, Srijan; Chen, Yuguo
• 署名单位: Virginia Polytechnic Institute & State University; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12245
• 发表日期: 2018
• 页码: 365-386
• 关键词: stochastic blockmodels; Consistency; likelihood

摘要

The community structure that is observed in empirical networks has been of particular interest in the statistics literature, with a strong emphasis on the study of block models. We study an important network feature called node popularity, which is closely associated with community structure. Neither the classical stochastic block model nor its degree-corrected extension can satisfactorily capture the dynamics of node popularity as observed in empirical networks. We propose a popularity-adjusted block model for flexible and realistic modelling of node popularity. We establish consistency of likelihood modularity for community detection as well as estimation of node popularities and model parameters, and demonstrate the advantages of the new modularity over the degree-corrected block model modularity in simulations. By analysing the political blogs network, the British Members of Parliament network and the Digital bibliography and library project' bibliographical network, we illustrate that improved empirical insights can be gained through this methodology.