Missing network data a comparison of different imputation methods

Robert W. Krause; Mark Huisman; Christian Steglich; Tom A.B. Sniiders

doi:10.1109/ASONAM.2018.8508716

Missing network data a comparison of different imputation methods

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

38 Scopus citations

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 language	English
Title of host publication	Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Editors	Andrea Tagarelli, Chandan Reddy, Ulrik Brandes
Pages	159-163
Number of pages	5
ISBN (Electronic)	9781538660515
DOIs	https://doi.org/10.1109/ASONAM.2018.8508716
State	Published - Oct 24 2018
Event	10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain Duration: Aug 28 2018 → Aug 31 2018

Publication series

Name	Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Conference

Conference	10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Country/Territory	Spain
City	Barcelona
Period	8/28/18 → 8/31/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Bayesian ERGM
data
exponential random graph model
multiple imputation
social networks

ASJC Scopus subject areas

Sociology and Political Science
Communication
Computer Networks and Communications
Information Systems and Management

Access to Document

10.1109/ASONAM.2018.8508716

Cite this

Krause, R. W., Huisman, M., Steglich, C., & Sniiders, T. A. B. (2018). Missing network data a comparison of different imputation methods. In A. Tagarelli, C. Reddy, & U. Brandes (Eds.), Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 (pp. 159-163). Article 8508716 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018). https://doi.org/10.1109/ASONAM.2018.8508716

Krause, Robert W. ; Huisman, Mark ; Steglich, Christian et al. / Missing network data a comparison of different imputation methods. Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. editor / Andrea Tagarelli ; Chandan Reddy ; Ulrik Brandes. 2018. pp. 159-163 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018).

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author = "Krause, {Robert W.} and Mark Huisman and Christian Steglich and Sniiders, {Tom A.B.}",

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Krause, RW, Huisman, M, Steglich, C & Sniiders, TAB 2018, Missing network data a comparison of different imputation methods. in A Tagarelli, C Reddy & U Brandes (eds), Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018., 8508716, Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, pp. 159-163, 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, 8/28/18. https://doi.org/10.1109/ASONAM.2018.8508716

Missing network data a comparison of different imputation methods. / Krause, Robert W.; Huisman, Mark; Steglich, Christian et al.
Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. ed. / Andrea Tagarelli; Chandan Reddy; Ulrik Brandes. 2018. p. 159-163 8508716 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Krause RW, Huisman M, Steglich C, Sniiders TAB. Missing network data a comparison of different imputation methods. In Tagarelli A, Reddy C, Brandes U, editors, Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. 2018. p. 159-163. 8508716. (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018). doi: 10.1109/ASONAM.2018.8508716

Missing network data a comparison of different imputation methods

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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