Missing Network Data A Comparison of Different Imputation MethodsKrause, R. W., Huisman, M., Steglich, C. & Snijders, T. A. B., 2018, p. 159-163. 5 p.
Research output: Contribution to conference › Paper › Professional
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.
|Number of pages||5|
|Publication status||Published - 2018|
|Event||2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining|
- Barcelona, Spain
Duration: 28-Aug-2018 → 31-Aug-2018
|Conference||2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining|
|Period||28/08/2018 → 31/08/2018|
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
28/08/2018 → 31/08/2018Barcelona, Spain
- Missing Data, Networks, ERGM, BERGM, multiple imputation
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