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Perspectives on Social Network Analysis for Observational Scientific Data

  • Lisa SinghEmail author
  • Elisa Jayne Bienenstock
  • Janet Mann
Chapter

Abstract

This chapter is a conceptual look at data quality issues that arise during scientific observations and their impact on social network analysis. We provide examples of the many types of incompleteness, bias and uncertainty that impact the quality of social network data. Our approach is to leverage the insights and experience of observational behavioral scientists familiar with the challenges of making inference when data are not complete, and suggest avenues for extending these to relational data questions. The focus of our discussion is on network data collection using observational methods because they contain high dimensionality, incomplete data, varying degrees of observational certainty, and potential observer bias. However, the problems and recommendations identified here exist in many other domains, including online social networks, cell phone networks, covert networks, and disease transmission networks.

Keywords

Social Network Social Network Analysis Community Detection Killer Whale Bottlenose Dolphin 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was funded by the Office of Naval Research under grant number #10230702 and the National Science Foundation under grant numbers #0941487 and #0918308.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Lisa Singh
    • 1
    Email author
  • Elisa Jayne Bienenstock
  • Janet Mann
  1. 1.Georgetown UniversityWashingtonUSA

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