Statistical Common Author Networks (SCAN)

TL;DR

This paper introduces a novel visualization method based on common author overlaps to analyze the relatedness of scientific fields, demonstrating its effectiveness through case studies in complexity science and neutrino physics.

Contribution

The paper develops a robust methodology for comparing scientific areas of different sizes and handling name homonymy, enabling accurate network visualizations of research relatedness.

Findings

Relatedness in complexity science varies widely, with many weakly connected areas.

Neutrino physics shows strongly interconnected subfields.

The method's results align with intuitive expectations of field relatedness.

Abstract

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.