***** To join INSNA, visit http://www.insna.org *****_____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.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 Qvalue in Netdraw’s GirvanNewman subgroup function. According to Hanneman’s online book on social network methods, this Qvalue is a goodness of fit.
Does anyone know how to interpret the Q value (or goodness of fit) in Netdraw's GirvanNewman 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
***** To join INSNA, visit http://www.insna.org *****This may have more information than you want, but this paper covers the modularity index very well.
Porter, M. A., J.P. Onnela, et al. (2009). "Communities in Networks." Notices of the American Mathematical Society, Vol. 56, No. 9, 2009.
Chris
From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of P. Lin
Sent: Wednesday, February 08, 2012 3:26 PM
To: [log in to unmask]
Subject: [SOCNET] GirvanNewman's Q value in Netdraw
***** To join INSNA, visit http://www.insna.org *****
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 Qvalue in Netdraw’s GirvanNewman subgroup function. According to Hanneman’s online book on social network methods, this Qvalue is a goodness of fit.
Does anyone know how to interpret the Q value (or goodness of fit) in Netdraw's GirvanNewman 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
_____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.
_____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.