3 M. Karnstedt C. Hayes 2009 Towards Cross-Community Effects in Scientific Communities KDML 2009: Knowledge Discovery, Data Mining, and Machine Learning* <p>Community effects on the behaviour of individu- als, the community itself and other communities can be observed in a wide range of applications. This is true in scientific research, where commu- nities of researchers have increasingly to justify their impact and progress to funding agencies. Previous work has tried to explain these phenom- ena 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. How- ever, there is still a great potential for increasing the quality and accuracy of this analysis, espe- cially 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 commu- nities and propose novel ideas to detect and ex- plain cross-community effects with a special fo- cus on their characteristics in a given timeline. The outcome is a roadmap for advanced analy- sis of cross-community effects, which promises valuable insights for all areas of scientific re- search.</p>

* Jointly funded by Lion-2 and Clique

 http://lwa09.informatik.tu-darmstadt.de/pub/KDML/WebHome/kdml09_M.Karnstedt_C.Hayes.pdf