Our article on “Statistical Common Author Networks” was published recently in the Journal of the American Society for Information Science and Technology. The article is available on the JASIST website to download or purchase here. Below is the abstract to give you an idea about what the article is about:
ABSTRACT: A new method for visualizing the relatedness of scientific areas has been 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 method 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 2 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, whereas the much narrower area of neutrino physics shows very strongly related areas.
Copyright restrictions prevent us from hosting a copy of the full article, but clients and colleagues can contact me directly to obtain an electronic copy of the full article.
Additionally, a pre-print is available for free on the arXiv pre-print server here.