  A new method for visualizing the relatedness of scientific areas is developed
that is based on measuring the overlap of researchers between areas. It is
found that closely related areas have a high propensity to share a larger
number of common authors. A methodology for comparing areas of vastly different
sizes and to handle name homonymy is constructed, allowing for the robust
deployment of this method on real data sets. A statistical analysis of the
probability distributions of the common author overlap that accounts for noise
is carried out along with the production of network maps with weighted links
proportional to the overlap strength. This is demonstrated on two case studies,
complexity science and neutrino physics, where the level of relatedness of
areas within each area is expected to vary greatly. It is found that the
results returned by this method closely match the intuitive expectation that
the broad, multidisciplinary area of complexity science possesses areas that
are weakly related to each other while the much narrower area of neutrino
physics shows very strongly related areas.
