***** To join INSNA, visit http://www.insna.org *****
PS. We further elaborated on these methods (using entropy statistics) in:
Koen Frenken & Loet Leydesdorff, Scaling Trajectories in Civil Aircraft
(1913-1997), Research Policy 29(3) (2000) 331-348.
You may find it useful to look into entropy statistics. I did a paper in
1991 entitled The Static and Dynamic Analysis of Network Data Using
Information Theory, Social Networks 13 (1991) 301-45. You can find it at my
website at http://www.leydesdorff.net/list.htm .
With best wishes,
Amsterdam School of Communications Research (ASCoR),
Kloveniersburgwal 48, 1012 CX Amsterdam.
Tel.: +31-20- 525 6598; fax: +31-20- 525 3681;
[log in to unmask] ; http://www.leydesdorff.net/
> -----Original Message-----
> From: Social Networks Discussion Forum
> [mailto:[log in to unmask]] On Behalf Of Nick Guenther
> Sent: Wednesday, March 29, 2006 3:16 AM
> To: [log in to unmask]
> Subject: Re: Advice needed regarding longitudinal network data
> ***** To join INSNA, visit http://www.insna.org *****
> On 3/28/06, Srikanth <[log in to unmask]> wrote:
> > ***** To join INSNA, visit http://www.insna.org *****
> > Hello,
> > I am trying to solve the following problem: I have
> relationship data
> > collected at various points in time regarding the participation of
> > different members in a discussion group. Data were accumulated
> > periodically, so the network at time T+1 consists of the mapping of
> > all discussions that have occurred in the period between T and T+1.
> > The data are in the form of adjacency matrices. I need to
> compare the
> > networks that existed at different time periods.
> I was just reading an article in Science by Gueorgi Kossinets
> and his advisor Duncan Watts that did a longitudinal study of
> a university (that I only discovered I had a copy of because
> of reading the archives here!). Here's the link:
> They had continuous data but most of their techniques should
> still apply to you. In fact, it occurs to me that you are
> probably wrong to just compare the network at T, T+1...
> because the network is probably not discrete like that, but
> instead each past state has a varying amount of influence on
> the present.
> You'll want to read the 'supporting online material' if you
> have access to it; it has the 'methods' section. Some excerpts:
> They define a "sampling period" tau to determine which links
> are considered ongoing links, and which are considered
> broken. In their study they chose Tau=60 days which they
> figured out by analyzing the distribution of communications
> (95% of responses came within 60 days).
> What is your goal in your study? I'm still just learning SNA
> but I keep hitting up against that the appropriate technique
> really really really depends on the data.
> -Nick Guenther
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