Missing Network Data A Comparison of Different Imputation Methods

Robert W. Krause, Mark Huisman, Christian Steglich, Tom A. B. Snijders

Research output: Contribution to conferencePaperProfessional

15 Citations (Scopus)
382 Downloads (Pure)

Abstract

This paper compares several imputation methods for missing data in network analysis on a diverse set of simulated networks under several missing data mechanisms. Previous work has highlighted the biases in descriptive statistics of networks introduced by missing data. The results of the current study indicate that the default methods (analysis of available cases and null-tie imputation) do not perform well with moderate or large amounts of missing data. The results further indicate that multiple imputation using sophisticated imputation models based on exponential random graph models (ERGMs) lead to acceptable biases even under large amounts of missing data.
Original languageEnglish
Pages159-163
Number of pages5
DOIs
Publication statusPublished - 2018
Event2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
- Barcelona, Spain
Duration: 28-Aug-201831-Aug-2018
http://asonam.cpsc.ucalgary.ca/2018/

Conference

Conference2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Abbreviated titleASONAM
CountrySpain
CityBarcelona
Period28/08/201831/08/2018
Internet address

Keywords

  • Missing Data
  • Networks
  • ERGM
  • BERGM
  • multiple imputation

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