Bayesian stochastic blockmodeling

Research output: Contribution to Book/Report typesChapterpeer-review

Abstract (may include machine translation)

This chapter describes the basic variants of the stochastic blockmodel (SBM), and a consistent Bayesian formulation that allows readers to infer them from data. The focus is on developing a framework to extract the large-scale structure of networks while avoiding both overfitting and underfitting, and doing so in a manner that is analytically tractable and computationally efficient. The Bayesian inference approach provides a methodologically correct answer to the very central question in network analysis of whether patterns of large-scale structure can in fact be supported by statistical evidence. Besides this practical aspect, it also opens a window into the fundamental limits of network analysis itself, giving readers a theoretical underpinning we can use to understand more about the nature of network systems. The chapter shows how inferring the SBM can be used to predict missing and spurious links and also sheds light on the fundamental limitations of the detectability of modular structures in networks.

Original languageEnglish
Title of host publicationAdvances in Network Clustering and Blockmodeling
Publisherwiley
Pages289-332
Number of pages44
ISBN (Electronic)9781119483298
ISBN (Print)9781119224709
DOIs
StatePublished - 13 Dec 2019
Externally publishedYes

Keywords

  • Bayesian inference approach
  • Bayesian stochastic blockmodeling
  • Microcanonical models
  • Minimum description length principle
  • Network analysis
  • Statistical evidence

Fingerprint

Dive into the research topics of 'Bayesian stochastic blockmodeling'. Together they form a unique fingerprint.

Cite this