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All,
Yes but everytime we think about this topic we realize the answer is: It
depends on the network being measured.  The same organization will have
an infinite number of networks depending on which network is being measured:
Who goes to whom for advice is one network, but
Who do you think has the most power is another.

- Tom

> *****  To join INSNA, visit http://www.sfu.ca/~insna/  *****
>
> A client recently asked me, "What type of networks do you run into in organizations?  Scale-free or Small-world?"  I did an informal browse of about 20 data sets from biz orgs and found many occurrences of a third pattern -- core-periphery!!  [C/P model as described by Borgatti & Everett] So guys, where is YOUR book?
>
> Both S-W and C/P have similar metrics -- short path lengths and high clustering.  Big difference is one[C/P] vs. many[S-W] clusters.  A more complete investigation would be interesting... how many data sets from how many situations would be sufficient?  Sounds like a good thesis topic.
>
> Valdis
>
>
> ---- David Lazer <[log in to unmask]> wrote:
>
>
>>(2) what networks tend to be scale free, and what networks not?  The
>>interpersonal data I tend to work with I'm pretty sure tend to be normally
>>distributed.  Many other kinds of networks, as Barabasi and others have
>>shown, are power law distributed in in-degree.  If one were to survey
>>social network data sets, and categorize them by type of distribution of
>>in-degree, what would the categories be, and what would be the variables
>>underlying those categories?  Has this been done?
>
>
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--
To learn more about my evaluation book go to:
http://www.oup-usa.org/isbn/0195141768.html
My personal webpage:
http://www-hsc.usc.edu/~tvalente/
---
Thomas W. Valente, PhD
Director, Master of Public Health Program
http://www.usc.edu/hsc/medicine/preventive_med/ipr/mph/
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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