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SOCNET  March 2006

SOCNET March 2006

Subject:

Re: Advice needed regarding longitudinal network data

From:

James Howison <[log in to unmask]>

Reply-To:

James Howison <[log in to unmask]>

Date:

Wed, 29 Mar 2006 18:05:43 -0500

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (106 lines)

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

On Mar 28, 2006, at 3:48 AM, Srikanth 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.

We have recently begun such an analysis, sticking specifically with  
the snapshots over time method (as opposed to the continuous methods)  
and aiming to describe an existing network over time, rather than  
model the evolution of a network (although we're interested in doing  
that eventually).

A pre-print is available here:

Howison, J., Inoue, K., and Crowston, K. (2006). Social dynamics of  
free and open source team communications. In Proceedings of the IFIP  
2nd International Conference on Open Source Software, June 8-10, Lake  
Como, Italy.
http://floss.syr.edu/publications/ 
howison_dynamic_sna_intoss_ifip_short.pdf

> This paper furthers inquiry into the social structure of free and  
> open source software (FLOSS) teams by undertaking social network  
> analysis across time. Contrary to expectations, we confirmed  
> earlier findings of a wide distribution of centralizations even  
> when examining the networks over time. The paper also provides  
> empirical evidence that while change at the center of FLOSS  
> projects is relatively uncommon, participation across the project  
> communities is highly skewed, with many participants appearing for  
> only one period. Surprisingly, large project teams are not more  
> likely to undergo change at their centers.

The most relevant reference from that paper is:

H. C. White, S. A. Boorman, and R. L. Brieger. Social structure from  
multiple networks I. Blockmodels of roles and positions. American  
Journal of Sociology, 81(4):730–780, 1976.

Here White specifically highlights the differences between a  
description over time and a modeling of change over time:

> White et al [15] introduced the modeling of social structure over  
> time using
> snapshot data. Our method is similar and their clear comment also  
> applies,
> we “present no models of processes over time; there are neither  
> predictions of
> other behavior nor explications of a stochastic process of tie  
> formation and
> dissolution” (p 732). Rather the analysis below seeks merely to  
> describe the
> structures as found at different points in time. Analysis of  
> networks over time
> with attention to causes and predictions from structure and its  
> change, such
> as preferential attachment, is an active area of research [11, 9]  
> and one that
> may be fruitful on this data.

One of the challenges that we were not able to address in this  
version of the paper is that the method of taking snapshots (and we  
took overlapping periods to smooth the data) yields time-series, yet  
at this stage we're using averages to describe the series over time.   
We hope to use better summary statistics and, indeed, time-series  
analysis on the full series in the future.  The data and analysis  
scripts are available: http://floss.syr.edu/publications/ 
howison_dynamic_sna_intoss_ifip.tgz

We also get into differences between calculations of network  
statistics collapsing across time (thus getting overall  
centralization etc) and doing so on snapshot data (and then  
calculating a average centralization) and present some ideal types of  
network features which go towards explaining the differences between  
average and overall centrality.  We argue that average figures, or at  
least descriptions over time, are preferable because they are far  
more likely to reflect the structure as experienced by the participants.

Feedback and criticism, of course, appreciated and welcomed.

Srikanth, it would be great if you can summarize what else you find  
about such longitudinal descriptive (as opposed to evolution  
modeling) back here (I know that some people email off-list sometimes).

Cheers,
James Howison
PhD Candidate
Syracuse University School of Information Studies
http://james.howison.name

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