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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. [ABSTRACT FROM PUBLISHER]

This paper introduces an agglomerative method for detecting cohesive subgroups in networks based on geodesic distance. The algorithm starts with a set of nodes as "seed". Beginning with the seed nodes as initial clusters, the clusters grow by incorporating more nodes successively based on minimal average distance to the current members of the cluster as a criterion for cluster extension. This approach is combined with an optimization step to achieve high quality performance on subgroup detection. The resulting method for detecting discrepant cohesive subgroups has been tested on artificial benchmark graphs as well as real-world networks. [ABSTRACT FROM PUBLISHER]