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Just to add that UCINET does calculate Q along with a host of other measures in the routine called cluster adequacy (Tools>Cluster Analysis>Cluster adequacy).

Given a partition of a proximity matrix of similarities into clusters, then this routine calculates the goodness of fit measures which try and capture the adequacy of the partitions. The measure calculated are eta, Newman and Girvan's  modularity Q, Krackhardt and Stern's E-I, Freemans segregation measure S and Cohen's Kappa

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From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of P. Lin
Sent: 08 February 2012 23:26
To: [log in to unmask]
Subject: [SOCNET] Girvan-Newman's Q value in Netdraw

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Hi,

I'm a PhD student at the University of Washington Information School studying high school social groups and the multiplexity of technology interactions.

I'm intrigued by the Q-value in Netdraw's Girvan-Newman subgroup function. According to Hanneman's online book on social network methods, this Q-value is a goodness of fit.
Does anyone know how to interpret the Q value (or goodness of fit) in Netdraw's Girvan-Newman subgroup function?
What is considered a good fit?

I got no responses on the UCINET group (posted 2 days ago), and emailed Dr. Hanneman, who kindly suggested I email this listserv.

To elaborate, Netdraw has an Analysis>Subgroup function that :

a) visualizes the subgroups partitioned by coloring the subgroups in different colors.
b) produces partition information, including a Q value.

I looked through the Girvan and Newman (2002) article cited for this algorithm, and couldn't see an explanation of the goodness of fit either.

UCINET does not produce this goodness of fit measurement either, which leads me to think that the goodness of fit is specific to Netdraw?

The Netdraw output makes sense to me (in the way I'm interpreting my data), and I'm just looking for a way to validate the clusters produced. So, perhaps the Q goodness of fit measure produced could help, though I need some help in knowing how that Q value is produced and what it means.

Thank you!


Peyina
-
Peyina Lin
PhD Candidate
The Information School
University of Washington
http://students.washington.edu/pl3
@peyinalin

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