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I have done some research in the area, together with colleages:
Zeini, S., Göhnert, T., Hecking, T., Krempel, L., & Hoppe, H. U. (2014).
The Impact of Measurement Time on Subgroup Detection in Online
Communities. In F. Can, T. Özyer, & F. Polat (Eds.), State of the Art
Applications of Social Network Analysis (P. 249–268). Springer
International Publishing. http://dx.doi.org/10.1007/978-3-319-05912-9_12
More and more communities use internet based services and infrastructure
for communication and collaboration. All these activities leave digital
traces that are of interest for research as real world data sources that
can be processed automatically or semi-automatically. Since productive
online communities (such as open source developer teams) tend to support
the establishment of ties between actors who work on or communicate
about the same or similar objects, social network analysis is a
frequently used research methodology in this field. A typical
application of Social Network Analysis (SNA) techniques is the detection
of cohesive subgroups of actors (also called “community detection”. We
were particularly interested in such methods that allow for the
detection of overlapping clusters, which is the case with the Clique
Percolation Method (CPM) and Link Community detection (LC). We have used
these two methods to analyze data from some open source developer
communities (mailing lists and log files) and have compared the results
for varied time windows of measurement. The influence of the time span
of data capturing/aggregation can be compared to photography: A certain
minimal window size is needed to get a clear image with enough “light”
(i.e. dense enough interaction data), whereas for very long time spans
the image will be blurred because subgroup membership will indeed change
during the time span (corresponding to a moving target). In this sense,
our target parameter is “resolution” of subgroup structures. We have
identified several indicators for good resolution. In general, this
value will vary for different types of communities with different
communication frequency and behavior. Following our findings, an
explicit analysis and comparison of the influence of time window for
different communities may be used to better adjust analysis techniques
for the communities at hand.
By request, I can send you a pre-published draft version.
Am 15.10.2015 um 13:05 schrieb Shahadat Uddin:
> ***** To join INSNA, visit http://www.insna.org *****
> Dear SOCNET users,
> At present I am conducting a literature review of approaches to select
> the right/correct window size for analysing a given longitudinal
> social network. I did not find many articles in this respect in the
> present literature. In fact, I did not find any approach/method that
> can determine an appropriate window size for analysing longitudinal
> social networks.
> Can anyone help me in this regard!!
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Freelance Social Network Analyst - Open Data hacktivist -
Adjunct lecturer at the University of Duisburg-Essen, Germany
Mail: [log in to unmask]
SOCNET is a service of INSNA, the professional association for social
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