Detecting emerging research fronts in regenerative medicine by citation network analysis of scientific publications

In today's increasingly global and knowledge-based economy, competitiveness and growth depend on the ability of an economy to meet fast-changing market needs quickly and efficiently through management of new science and technology. Therefore, for both R&D managers and policy makers, noticing emerging research domains among numerous academic papers has become a significant task. However, such a task becomes highly laborious and difficult as each research domain becomes specialized and segmented. In this paper, we detect emerging research front from a huge number of academic papers related regenerative medicine, which is a case of radically innovative research. We divide citation networks into clusters using the topological clustering method, track the positions of papers in each cluster, and visualize citation networks with characteristic terms for each cluster. Analyzing the clustering results with the average age and parent-children relationship of each cluster could be helpful in detecting emergence. In addition, tracking topological measures, within-cluster degree z and participation coefficient P, enable us to determine whether there are emerging knowledge clusters. Our results show that our method succeeds to detect emerging research fronts in regenerative medicine and these results are confirmed as reasonable ones by experts.