Generating Local Maps of Science using Deep Bibliographic Coupling
This incremental improvement addresses the problem of limited connectivity in science mapping for researchers and analysts.
The authors tackled the limitation of bibliographic coupling methods, which require shared references to measure similarity between papers, by extending it to deep neighborhoods using graph diffusion, enabling similarity measurement for any two papers and generating local maps of science.
Bibliographic and co-citation coupling are two analytical methods widely used to measure the degree of similarity between scientific papers. These approaches are intuitive, easy to put into practice, and computationally cheap. Moreover, they have been used to generate a map of science, allowing visualizing research field interactions. Nonetheless, these methods do not work unless two papers share a standard reference, limiting the two papers usability with no direct connection. In this work, we propose to extend bibliographic coupling to the deep neighborhood, by using graph diffusion methods. This method allows defining similarity between any two papers, making it possible to generate a local map of science, highlighting field organization.