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We have a paper forthcoming in Social Networks on this topic. We find that, in
general, centrality measure accuracy decrease linearly with nodes missing at
random. Some measures do better than others. In degree, for example is quite
stable retaining a high correlation with the full sample when as much as 50% of
the data (respondents) are missing at random. A simple eigenvector, as a
measure of centrality, seems to do quite well. The more a centrality measured
tapped the structure of the network, the more unstable it became. We assessed 11
centrality measures on 59 social networks measured in 7 different studies
Costenbader, E. & Valente, T. W. (in press). The stability of centrality when
networks are sampled. Social Networks.
- Tom V.
Bergin, Sean wrote:
> Hi all,
> Would love some help on this:
> I am thinking about doing some work on using simulation and resampling
> methods for developing confidence measures for network metrics in
> networks with missing data. I.e. what is the range of effect of removing
> x proportion of nodes in a network of size y. Does anyone know of any
> work on this to date?
> Thanking you all in advance
To learn more about my evaluation book go to:
My personal webpage:
Thomas W. Valente, PhD
Director, Master of Public Health Program
Department of Preventive Medicine
School of Medicine
University of Southern California
1000 Fremont Ave.
Building A Room 5133
Alhambra CA 91803
phone: (626) 457-6678
fax: (626) 457-6699
email: [log in to unmask]
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