Towards cross-community effects in scientific communities

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. Previous work has tried to explain these phenomena by analysing co-citation graphs with methods from social network analysis and graph mining. More recent approaches have supplemented this with techniques from textual clustering. However, 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 present existing approaches and discuss their strengths and weaknesses. Based on this, we choose two closely related communities and propose novel ideas to detect and explain cross-community effects with a special focus on their characteristics in a given timeline. The outcome is a roadmap for advanced analysis of cross-community effects, which promises valuable insights for all areas of scientific research.