  We explore the statistical structure of scientific community based on
multivariate analysis of publication (or other identifiable metrics)
distribution in the author space. Here, we define community based on keywords,
i.e. projecting semantic content of the documents on predefined meanings;
however, more complex approaches based on semantic clustering of publications
are possible. Remarkably, this simple statistical analysis of publication
metadata allows understanding of internal interactions with community in
general agreement with experience acquired over decades of social interaction
within it. We further discuss potential applications of this approach for
ranking within the community, reviewer selection, and optimization of community
output.
