*****  To join INSNA, visit http://www.insna.org  *****

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, 


Loet

________________________________
Loet Leydesdorff 
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:
> http://www.sciencemag.org/cgi/content/short/311/5757/88
> 
> 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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