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image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset
Data sources: ZENODO
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physics-cocitations

Authors: Nakajima, Kazuki;

physics-cocitations

Abstract

Summary:The physics-cocitations dataset is a co-citation hypergraph of highly cited Physics and Astronomy papers, constructed from the OpenAlex Snapshot (2024-09-27; https://developers.openalex.org/download/snapshot-format). Nodes and Hyperedges:Nodes represent highly cited Physics and Astronomy papers. For each Physics and Astronomy subfield, papers were ranked by citation count, and those accounting for the top 10% of cumulative citations were selected as nodes (minimum 100 papers per subfield), yielding 5,347 nodes in total. Hyperedges represent co-citation relationships. For each paper in the OpenAlex corpus (excluding the selected top papers), the subset of top papers it cites was identified. If the same subset appeared in at least 3 citing papers, it was included as a hyperedge, yielding 42,535 hyperedges in total. Each node carries the following attributes: OpenAlex work ID, paper title, publication date, primary topic, subfield, field, domain, and citation count. Basic statistics:- Nodes: 5,347- Hyperedges: 42,535 Source:OpenAlex Snapshot (2024-09-27), https://developers.openalex.org/download/snapshot-format Reference:Kazuki Nakajima, Yuya Sasaki, Takeaki Uno, and Masaki Aida. (2025). Learning Multi-Order Block Structure in Higher-Order Networks. arXiv preprint arXiv:2511.21350.

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