  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.
