You’re right. Looking at the formula for eigenvector centrality, the centrality of each node is completely a linear function of the centralities of its neighbors. However, this dependence is not a problem: it’s the pattern of interdependence that eigenvector centrality models. For eigenvector centrality to be a good model for a phenomenon (be it social standing or information or importance in the functioning network or religiosity), the status of a node must be function of the status of its neighbors and not a function of other indirect or un-modelled connections. I suggest you look at the pattern of interdependence in religious practice. Is the pattern for a node a function only of the pattern of its neighbors and not of indirect connections? If this is true then eigenvector centrality is one way of assessing the overall influence of each node.
Professor Emeritus of Sociology
University of California, Los Angeles
From: Social Networks Discussion Forum [mailto:[log in to unmask]]
On Behalf Of Elly Power
Sent: Monday, March 16, 2015 6:17 PM
To: [log in to unmask]
Subject: Centrality as the Dependent Variable?
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I was hoping I could get some advice on how (or if) I could use centrality measures (e.g., eigenvector centrality) as the dependent variable in some analyses.
I know that we usually think of centrality as an independent variable, but it seems reasonable that we might want to predict centrality. Personally, I work on religious practice, and I want to understand if the nature of someone's religious practice might influence his/her centrality.
The issue, of course, is that centrality measures are not independent. Does anyone know of any ways to deal with this? Is there anyone who has tried to look at this? Any direction would be very much appreciated.
Thanks in advance for all of your suggestions.
- Elly Power
Eleanor A. Power, PhD Candidate
Department of Anthropology
450 Serra Mall, Bldg 50
Stanford, CA 94305
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