ArticleSocial networks2022
Network sampling coverage III: Imputation of missing network data under different network and missing data conditions.
Article in Social networks, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 27 citations in OpenAlex.
- Spreading potential in disease relevant networks: Predicting centralities in rural Northeast Madagascar.PLOS global public health · 2026Article
- Considerations for Social Networks and Health Data Sharing: An Overview.Annals of epidemiology · 2025Review
- A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.Movement ecology · 2024Article
- Expanding the Evidence Base for Improving Sexual Health Among Transgender Communities: The Importance of Rigorous Epidemiologic Studies.The Journal of infectious diseases · 2024Article
- Disentangling associations between pubertal development, healthy activity behaviors, and sex in adolescent social networks.PloS one · 2024Article
- Revealing the Relational Mechanisms of Research for Development Through Social Network Analysis.The European journal of development research · 2023Article
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Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
Missing data is a common, difficult problem for network studies. Unfortunately, there are few clear guidelines about what a researcher should do when faced with incomplete information. We take up this problem in the third paper of a three-paper series on missing network data. Here, we compare the performance of different imputation methods across a wide range of circumstances characterized in terms of measures, networks and missing data types. We consider a number of imputation methods, going from simple imputation to more complex model- based approaches. Overall, we find that listwise deletion is almost always the worst option, while choosing the best strategy can be difficult, as it depends on the type of missing data, the type of network and the measure of interest. We end the paper by offering a set of practical outputs that researchers can use to identify the best imputation choice for their particular research setting.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.