Constantino et al., "Representing the Disciplinary Structure of Physics" (2025)

Isabel Constantino, Sadamori Kojaku, Santo Fortunato, and Yong-Yeol Ahn, Quantitative Science Studies 6, 263–280 (2025)
DOI | arXiv

@article{constantino2025representing,
    author = {Isabel Constantino and Sadamori Kojaku and Santo Fortunato and Yong-Yeol Ahn},
    title = {Representing the Disciplinary Structure of Physics: A Comparative Evaluation of Graph and Text Embedding Methods},
    journal = {Quantitative Science Studies},
    volume = {6},
    pages = {263--280},
    doi = {10.1162/qss_a_00349},
    year = {2025},
}

Physics, Graph embedding, node2vec, Map of science, PACS code

Do graph and text embeddings capture consistent or divergent aspects of scientific structure? Using the Physics and Astronomy Classification Scheme (PACS) from American Physical Society publications, the paper compares both families of methods as ways to represent the disciplinary structure of physics. Neural network-based methods outperform traditional methods, and graph embedding methods such as node2vec capture the PACS structure better than the text-based methods they test.