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See these papers:
Stephen Borgatti, Kathleen M. Carley and David Krackhardt, 2006,
“Robustness of Centrality Measures under Conditions of Imperfect Data,”
Social Networks, 28.2:124-136. DOI: 10.1016/j.socnet.2005.05.001
Terrill L. Frantz and Kathleen M. Carley, 2010, “Toward A Confidence
Estimate For The Most-Central-Actor Finding,” Sage Publications/RM
division Best Student Paper Proceedings of the Academy of Management
Annual Conference, Chicago, IL, USA.
Terrill L. Frantz, Marcelo Cataldo and Kathleen M. Carley, 2009,
“Robustness of centrality measures under uncertainty: Examining the role
of network topology,” Computational and Mathematical Organization
Theory, 15.4:303-328. DOI: 10.1007/s10588-009-9063-5
On 2/11/2013 10:56 PM, Qian Hu wrote:
> ***** To join INSNA, visit http://www.insna.org *****
> Dear SOCNET members,
> I have two questions about the missing network data.
> what can be done if we cannot get an ideal response rate (say less than
> 70 percent, or even lower)?
> 1. I remember somewhere in the literature that says some centrality
> measures such as the indegree centrality, are relatively robust for
> network data that captures over 50% of the data. Am I correct?
> 2. The existing research seems to suggest two methods to deal with
> missing data: exponential random graph models, and imputation
> methods. If we know the key actors are captured in the imperfect
> data set. Can we leave them as they are without using any additional
> modeling process? Any literature to support this argument?
> Thank you very much.
> Qian Hu
> University of Central Florida
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