ArticleScience advances2021
Neural embeddings of scholarly periodicals reveal complex disciplinary organizations.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed.
- The gender gap in scholarly self-promotion on social media.Nature communications · 2025Article
- Gender differences in resume language and gender gaps in salary expectations.Journal of the Royal Society, Interface · 2025Article
- Quantifying the use and potential benefits of artificial intelligence in scientific research.Nature human behaviour · 2024Article
- Network community detection via neural embeddings.Nature communications · 2024Article
- Machine learning misclassification networks reveal a citation advantage of interdisciplinary publications only in high-impact journals.Scientific reports · 2024Article
- Promotional language and the adoption of innovative ideas in science.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Hidden citations obscure true impact in science.PNAS nexus · 2024Article
- Understanding scholar-trajectories across scientific periodicals.Scientific reports · 2024Article
- Data, measurement and empirical methods in the science of science.Nature human behaviour · 2023Review
- SciSciNet: A large-scale open data lake for the science of science research.Scientific data · 2023Article
- Non-White scientists appear on fewer editorial boards, spend more time under review, and receive fewer citations.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Gender inequality and self-publication are common among academic editors.Nature human behaviour · 2023Article
- A dataset of mentorship in bioscience with semantic and demographic estimations.Scientific data · 2022Article
- Deep forecasting of translational impact in medical research.Patterns (New York, N.Y.) · 2022Article
- CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling.Entropy (Basel, Switzerland) · 2022Article
- Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method.Frontiers in public health · 2022Article
- Understanding the onset of hot streaks across artistic, cultural, and scientific careers.Nature communications · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.