# Generating Local Maps of Science using Deep Bibliographic Coupling

arXiv: https://arxiv.org/abs/2109.10007 · HTML: https://mlsanity.com/paper/2109.10007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [What Do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published between 2006 and 2018](https://mlsanity.com/paper/1906.04588/) (2019)
- [Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge?](https://mlsanity.com/paper/1511.05078/) (2015)
- [The Closer the Better: Similarity of Publication Pairs at Different Co-Citation Levels](https://mlsanity.com/paper/1707.03076/) (2017)

## Built on by (most cited first, 1 of 1)

- [Index t-SNE: Tracking Dynamics of High-Dimensional Datasets with Coherent Embeddings](https://mlsanity.com/paper/2109.10538/) (2021)

## Abstract (arXiv)

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.