LISTSERV mailing list manager LISTSERV 16.0

Help for SOCNET Archives


SOCNET Archives

SOCNET Archives


SOCNET@LISTS.UFL.EDU


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Monospaced Font

LISTSERV Archives

LISTSERV Archives

SOCNET Home

SOCNET Home

SOCNET  July 2001

SOCNET July 2001

Subject:

Re: optimal network size?

From:

Carter Butts <[log in to unmask]>

Reply-To:

[log in to unmask]

Date:

Fri, 27 Jul 2001 17:54:34 -0400

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (77 lines)

Although Pat has pretty well summarized my basic position (the network
boundaries of interest to you should be based on your prior knowledge of
the processes under study), I wanted to use a few brave statements of
Bill's to add to the profusion of soapbox commentary:

Bill Richards wrote:

 > Stacy --
 >
 > You are right about 10 being too small. There is simply not enough
 > room in a network of 10 people for anything particularly
 > interesting or complex to happen.
 >

Irrespective of the truth or falsity of this (what does one mean by
"interesting or complex"?), I think it is important to point out that
this is irrelevant for many research questions (the present one possibly
excepted). If one is studying a phenomenon which acts on networks of
size 10 or less, I would think that one would then need to study
networks of size 10 or less; if this means that certain "interesting" or
"complex" things are eliminated, then so what? Many "interesting"
things can't happen within certain types of systems, but this doesn't
give one license to arbitrarily decide that one's system is other than
the data says it is. (You can't have attractors within Hamiltonian
systems, for example, but this doesn't mean that we can declare the
undamped pendulum to be non-Hamiltonian because we're bored with it.
Undamped pendula _are_ boring! :-)) Note that I would say that you
certainly _should_ switch models if the _data_ suggests that your
initial model is too restrictive....but the decision should not be based
on how "interesting" or otherwise aesthetically appealing the new model
seems.

Incidentally, I don't think that Bill intended his comments to apply
beyond the present context (in which the poster indicated an explicit
desire to maximize differences in scores), but I thought it important to
pontificate about this just in case. :-)


 > A network with 120 will be large enough for a range of interesting
 > phenomena to be seen. Do you want to just demonstrate an analytic
 > technique or are you interested in studying real-world phenomena?
 > For the former, use a network that is big enough to let you
 > demonstrate the abilities of your technique. For the latter, use a
 > network that is more than "just large enough" to show the kind of
 > things you are interested in. If you don't, you take the risk of
 > trivializing your problem or stacking the dice by limiting the
 > range of what is possible.
 >
 > Personally, I like bigger better than smaller.

I wonder about this. While there are certainly more large networks than
small ones (and more ways to get unusual behavior), the distributions of
  many network features tend to become much more concentrated in large
graphs; hence, a priori you may find that there is _less_ to see in a
large structure than in a small one. (Again, whether or not that is a
legitimate concern is another matter.) Certainly, this seems to be the
for a number of graph-level indices (degree centralization, Krackhardt
hierarchy, Krackhardt connectedness, betweenness centralization) over
the set of all graphs conditional on order and density. For degree (and
hence, with some tweaks, degree centralization) the convergence follows
pretty obviously from the law of large numbers, but simulation results
suggest increasing concentration for the others as well. (Everett,
Borgatti, and whomever also have some interesting findings about the
very high correlations between (node-level) degree and betweenness
scores in large graphs, which I wish they'd publish. Hint hint.)

Of course, social networks could be exceptions to the rule, but most of
the ones I've looked at don't seem to be. <shrug> Given the
combinatorial constraints, large interpersonal networks would almost
_have_ to exhibit increasing concentration on most of the standard
measures. Someone with a counterexample is probably out there somewhere
waiting to whack me over the head, but I'd still tend to bet with the
house on this one.

OK, I'll get off the soapbox now. Prepare the 2x4....

     -Carter

Top of Message | Previous Page | Permalink

Advanced Options


Options

Log In

Log In

Get Password

Get Password


Search Archives

Search Archives


Subscribe or Unsubscribe

Subscribe or Unsubscribe


Archives

October 2021
September 2021
August 2021
July 2021
June 2021
May 2021
April 2021
March 2021
February 2021
January 2021
December 2020
November 2020
October 2020
September 2020
August 2020
July 2020
June 2020
May 2020
April 2020
March 2020
February 2020
January 2020
December 2019
November 2019
October 2019
September 2019
August 2019
July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008, Week 62
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
December 2006
November 2006
October 2006
September 2006
August 2006
July 2006
June 2006
May 2006
April 2006
March 2006
February 2006
January 2006
December 2005
November 2005
October 2005
September 2005
August 2005
July 2005
June 2005
May 2005
April 2005
March 2005
February 2005
January 2005
December 2004
November 2004
October 2004
September 2004
August 2004
July 2004
June 2004
May 2004
April 2004
March 2004
February 2004
January 2004
December 2003
November 2003
October 2003
September 2003
August 2003
July 2003
June 2003
May 2003
April 2003
March 2003
February 2003
January 2003
December 2002
November 2002
October 2002
September 2002
August 2002
July 2002
June 2002
May 2002
April 2002
March 2002
February 2002
January 2002
December 2001
November 2001
October 2001
September 2001
August 2001
July 2001
June 2001
May 2001

ATOM RSS1 RSS2



LISTS.UFL.EDU

CataList Email List Search Powered by the LISTSERV Email List Manager