Missing data augmentation for bayesian exponential random multi-graph models

Robert W. Krause, Alberto Caimo

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

In this paper we present an estimation algorithm for Bayesian exponential random multi-graphs (BERmGMs) under missing network data. Social actors are often connected with more than one type of relation, thus forming a multiplex network. It is important to consider these multiplex structures simultaneously when analyzing a multiplex network. The importance of proper models of multiplex network structures is even more pronounced under the issue of missing network data. The proposed algorithm is able to estimate BERmGMs under missing data and can be used to obtain proper multiple imputations for multiplex network structures. It is an extension of Bayesian exponential random graphs (BERGMs) as implemented in the Bergm package in R. We demonstrate the algorithm on a well-known example, with and without artificially simulated missing data.

Original languageEnglish
Title of host publicationSpringer Proceedings in Complexity
Pages63-72
Number of pages10
DOIs
StatePublished - 2019

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692

Bibliographical note

Funding Information:
We would like to thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high performance computing cluster.

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications

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