Comparison of Methods for Imputing Social Network Data

Xu, Ziqian; Hai, Jiarui; Yang, Yutong; Zhang, Zhiyong

doi:10.6339/22-JDS1045

Journal of Data Science

Comparison of Methods for Imputing Social Network Data

Volume 21, Issue 3 (2023): Special Issue: Advances in Network Data Science, pp. 599–618

Ziqian Xu Jiarui Hai Yutong Yang All authors (4)

https://doi.org/10.6339/22-JDS1045

Pub. online: 20 April 2022 Type: Data Science Reviews

Open Access

Received
21 December 2021

Accepted
30 March 2022

Published
20 April 2022

Abstract

Social network data often contain missing values because of the sensitive nature of the information collected and the dependency among the network actors. As a response, network imputation methods including simple ones constructed from network structural characteristics and more complicated model-based ones have been developed. Although past studies have explored the influence of missing data on social networks and the effectiveness of imputation procedures in many missing data conditions, the current study aims to evaluate a more extensive set of eight network imputation techniques (i.e., null-tie, Reconstruction, Preferential Attachment, Constrained Random Dot Product Graph, Multiple Imputation by Bayesian Exponential Random Graph Models or BERGMs, k-Nearest Neighbors, Random Forest, and Multiple Imputation by Chained Equations) under more practical conditions through comprehensive simulation. A factorial design for missing data conditions is adopted with factors including missing data types, missing data mechanisms, and missing data proportions, which are applied to generated social networks with varying numbers of actors based on 4 different sets of coefficients in ERGMs. Results show that the effectiveness of imputation methods differs by missing data types, missing data mechanisms, the evaluation criteria used, and the complexity of the social networks. More complex methods such as the BERGMs have consistently good performances in recovering missing edges that should have been present. While simpler methods like Reconstruction work better in recovering network statistics when the missing proportion of present edges is low, the BERGMs work better when more present edges are missing. The BERGMs also work well in recovering ERGM coefficients when the networks are complex and the missing data type is actor non-response. In conclusion, researchers analyzing social networks with incomplete data should identify the network structures of interest and the potential missing data types before selecting appropriate imputation methods.

Supplementary material

Supplementary Material

• supplement.pdf: Supplementary analyses, tables, and figures mentioned in the paper. • code: Code used in this study. This folder contains a README.txt file which explains how the code can be used.

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2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

Bayesian ERGM ERGM missing data multiple imputation

Funding

The research was supported by a grant from the US Department of Education (R305D210023).

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