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Dear Steve,

I'd also add the paper:

"A nonparametric view of network models and Newman–Girvan and other 
modularities"

by Peter Bickel and Aiyou Chen in PNAS 
<http://www.pnas.org/content/106/50/21068>.

They review some statistical issues in community detection, and 
construct a framework within various modularites, including 
Newman-Girvan, can be compared. They propose an alternative 
"likelihood modularity" and show it has good properties.

The various latent space or latent class models may be also of interest 
(see, e.g., <http://www.jstatsoft.org/v24/i05/>).

Good luck!

Mark


On 1/1/10 7:48 AM, Steve Eichert wrote:
> ***** To join INSNA, visit http://www.insna.org ***** Hello,
>
> I've been using the Newman Girvan algorithm [1] to identify communities
> within networks of individuals, however, I've been told by someone who I
> respect greatly that Newman Girvan isn't the best algorithm to use for
> identifying communities when dealing with human networks.  So, the
> question I have for the group is: what algorithm would you recommend for
> identifying communities when working with networks of people.
>
> [1] http://en.wikipedia.org/wiki/Girvan–Newman_algorithm
>
> All the best,
> Steve
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