Community effects on the behaviour of individuals, the community itself and other communities can be observed in a wide range of applications. This is true in scientific research, where communities of researchers have increasingly to justify their impact and progress to funding agencies. 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, especially in the context of cross-community effects. In this work, we propose a general framework consisting of several different techniques to analyse and explain such dynamics. The proposed methodology works with arbitrary community algorithms and incorporates meta-data to improve the overall quality and expressiveness of the analysis. We suggest and discuss several approaches to understand, interpret and explain particular phenomena, which themselves are identified in an automated manner. We illustrate the benefits and strengths of our approach by exposing highly interesting in-depth details of cross-community effects between two closely related and well established areas of scientific research. We finally conclude and highlight the important open issues on the way towards understanding, defining and eventually predicting typical life-cycles and classes of communities in the context of cross-community effects.