ArticleSocial networks2017
Network sampling coverage II: The effect of non-random missing data on network measurement.
Article in Social networks, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Interorganizational Networks in Physical Activity Promotion: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
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- Spreading potential in disease relevant networks: Predicting centralities in rural Northeast Madagascar.PLOS global public health · 2026Article
- A dataset of a whole network to investigate healthy and unhealthy behaviors in an Italian Community.Frontiers in sociology · 2026Article
- Data quality assessment of pig movements in the Norwegian Livestock Register reveals challenges for outbreak preparedness.BMC veterinary research · 2025Article
- Optimization of multiple sampling for solving network boundary specification problem.Scientific reports · 2025Article
- 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
- Ambiguous landscapes: A framework for assessing robustness and uncertainties in archaeological point pattern analysis.PloS one · 2024Article
- Examining preconditions for integrated care: a comparative social network analysis of the structure and dynamics of strong relations in child service networks.BMC health services research · 2023Article
- Embedded Distress: Social Integration, Gender, and Adolescent Depression.Social forces; a scientific medium of social study and interpretation · 2023Article
- Does gender structure social networks across domains of cooperation? An exploration of gendered networks among matrilineal and patrilineal Mosuo.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
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- Extent and implications of omitted ties on network measures in a longitudinal social network survey of people who use drugs.Drug and alcohol dependence · 2022Article
- Using Social Networks to Sample Migrants and Study the Complexity of Contemporary Immigration: An Evaluation Study.Demography · 2022Article
- Intolerant baboons avoid observer proximity, creating biased inter-individual association patterns.Scientific reports · 2022Article
- Intersectoral collaboration at a decentralized level: information flows in child welfare and healthcare networks.BMC health services research · 2022Article
- Linking parasitism to network centrality and the impact of sampling bias in its interpretation.PeerJ · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.
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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.