Brokerage, Boundary Spanning, and Leadership in Open Innovation Communities
Organization Science
Tracking the Evolution of Communities in Dynamic Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Analyzing the flow of ideas and profiles of contributors in an open learning community
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Designing pedagogical interventions to support student use of learning analytics
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Computational approaches to connecting levels of analysis in networked learning communities
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
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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 SNA techniques is the detection of cohesive subgroups of actors (also called "community detection"). A relatively new method for detecting cohesive subgroups is the Clique Percolation Method (CPM), which allows for detecting overlapping subgroups. We have used CPM 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. Applying these indicators to the different CPM results shows the best resolution is a time span of around 2-3 months. 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.