Measuring a community’s ’robustness’
Intervenant(s) : Rolf Pawelzik
- Date : Jeudi 12 juillet 2012
- Horaire : 15h20
- Durée : 40 minutes
- Lieu : Uni Mail 2160
This study of FOSS community evolution explores the applicability of a simple idea to define a network based indicator for a community’s ’robustness’. Instead of looking just at numbers (growing membership and activities) this study looks at the evolution of a community’s division-of-labor (the distribution of activities). To do so, we focus on knowledge exchange interactions compiled from the project’s email archives and investigate the evolution of the topology of the ’knowledge flow’ network.
The idea consists in comparing the topology of the community’s ’knowledge flow’ network with two artificial topologies as benchmarks : a core-periphery structure and a scale-free structure. The assumption is that in the early stages of a community knowledge sharing activities are distributed extremely unevenly among it’s members (core-periphery) but that over time this activity distribution changes and transforms into a more ’robust’ structure (scale-free). A community’s ’robustness’ is understood in terms of knowledge source redundancy : the more members participate in knowledge sharing activities, the more ’robust’ the community is against drop-outs of single knowledge sources.
This study tests this assumption and provides a statistic, which indicates for each community how well it’s knowledge exchange requirements have been fulfilled by it’s members in the past and possible future, and more importantly, it allows to compare different communities among each other and to characterize differences in their progression to increased ’robustness’.
This talk will present the results of the application of this idea to four communities : (to be determined, preferably related to the health domain), and derives conclusions about it’s suitability as a simple ’success’ indicator.
The speaker is researching FOSS communities at the public research center Henri Tudor in Luxembourg. He has a background in computer science and software engineering, especially of community portals, and is currently applying his knowledge to develop suitable data mining and analysis software to study the activities of online collaboration groups.