  In recent years there has been a growing interest in the role of networks and
clusters in the global economy. Despite being a popular research topic in
economics, sociology and urban studies, geographical clustering of human
activity has often studied been by means of predetermined geographical units
such as administrative divisions and metropolitan areas. This approach is
intrinsically time invariant and it does not allow one to differentiate between
different activities. Our goal in this paper is to present a new methodology
for identifying clusters, that can be applied to different empirical settings.
We use a graph approach based on k-shell decomposition to analyze world
biomedical research clusters based on PubMed scientific publications. We
identify research institutions and locate their activities in geographical
clusters. Leading areas of scientific production and their top performing
research institutions are consistently identified at different geographic
scales.
