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

Producción científica: Conference contribution › revisión exhaustiva

45 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Editores	Andrea Tagarelli, Chandan Reddy, Ulrik Brandes
Páginas	159-163
Número de páginas	5
ISBN (versión digital)	9781538660515
DOI	https://doi.org/10.1109/ASONAM.2018.8508716
Estado	Published - oct 24 2018
Evento	10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain Duración: ago 28 2018 → ago 31 2018

Serie de la publicación

Nombre	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
País/Territorio	Spain
Ciudad	Barcelona
Período	8/28/18 → 8/31/18

Nota bibliográfica

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

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

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10.1109/ASONAM.2018.8508716

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Krause, R. W., Huisman, M., Steglich, C., & Sniiders, T. A. B. (2018). Missing network data a comparison of different imputation methods. En 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). Artículo 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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title = "Missing network data a comparison of different imputation methods",

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.",

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note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 ; Conference date: 28-08-2018 Through 31-08-2018",

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Krause, RW, Huisman, M, Steglich, C & Sniiders, TAB 2018, Missing network data a comparison of different imputation methods. En 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).

Producción científica: Conference contribution › revisión exhaustiva

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N2 - 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.

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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KW - exponential random graph model

KW - multiple imputation

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Krause RW, Huisman M, Steglich C, Sniiders TAB. Missing network data a comparison of different imputation methods. En Tagarelli A, Reddy C, Brandes U, editores, 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

Resumen

Serie de la publicación

Conference

Nota bibliográfica

ASJC Scopus subject areas

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