Imputation of missing network data: Some simple procedures


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Journal of Social Structure

International Network for Social Network Analysis

Subject: Social Sciences


eISSN: 1529-1227





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VOLUME 10 , ISSUE 1 (December 2009) > List of articles

Imputation of missing network data: Some simple procedures

Mark Huisman

Keywords : Missingdata; Singleimputation; Descriptivenetworkanalysis; Friendship network

Citation Information : Journal of Social Structure. Volume 10, Issue 1, Pages 1-29, DOI:

License : (CC BY-NC 4.0)

Published Online: 10-January-2020



Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: model-based methods within the framework of exponential random graph models, and imputation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for non-response in a few specific situations.

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