  Understanding the structure of knowledge domains is one of the foundational
challenges in 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
as well as 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 science
of science, our framework may in turn facilitate the study of how knowledge is
created and organized.
