  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.
