Neural embeddings of scholarly periodicals reveal complex disciplinary organizations

Understanding the structure of knowledge domains is one of the foundational challenges in the science of science. Here, we propose a neural embedding technique that leverages the information contained in the citation network to obtain continuous vector representations of scientific periodicals. We demonstrate that our periodical embeddings encode nuanced relationships between periodicals and the complex disciplinary and interdisciplinary structure of science, allowing us to make cross-disciplinary analogies between periodicals. Furthermore, we show that the embeddings capture meaningful axes that encompass knowledge domains, such as an axis from soft to hard sciences or from social to biological sciences, which allow us to quantitatively ground periodicals on a given dimension. By offering novel quantification in the science of science, our framework may, in turn, facilitate the study of how knowledge is created and organized.

Automated summary

Understanding knowledge domains' structure is a fundamental challenge in the science of science. A neural embedding technique using citation network information to obtain vector representations of scientific periodicals is proposed. These embeddings encode nuanced relationships between periodicals and the complex disciplinary structure of science, allowing for cross-disciplinary analogies. Additionally, meaningful axes encompassing knowledge domains can be identified, enabling quantitative grounding of periodicals.