Life-cycles and mutual effects of scientific communities

Cross-community effects on the behaviour of individuals and communities themselves can be observed in a wide range of applications. While previous work has tried to explain and analyse such phenomena, there is still a great potential for increasing the quality and accuracy of this analysis. In this work, we propose a general framework consisting of several different techniques to analyse and explain cross-community effects and the underlying dynamics. The proposed methodology works with arbitrary community algorithms, incorporates meta-data to improve the overall quality and expressiveness of the analysis and identifies particular phenomena in an automated manner. We illustrate the benefits and strengths of our approach by exposing in-depth details of cross-community effects between two closely related and well established areas of scientific research. This work focuses on techniques for understanding, defining and eventually predicting typical life-cycles and events in the context of cross-community dynamics.