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Coupette, C.* ; Vreeken, J.* ; Rieck, B.

All the world's a (hyper)graph: A data drama.

Digit. Scholarsh. Humanit. 39, 74-96 (2024)
Verlagsversion DOI
Open Access Hybrid
Creative Commons Lizenzvertrag
We introduce HYPERBARD, a dataset of diverse relational data representations derived from Shakespeare's plays. Our representations range from simple graphs capturing character co-occurrence in single scenes to hypergraphs encoding complex communication settings and character contributions as hyperedges with edge-specific node weights. By making multiple intuitive representations readily available for experimentation, we facilitate rigorous representation robustness checks in graph learning, graph mining, and network analysis, highlighting the advantages and drawbacks of specific representations. Leveraging the data released in HYPERBARD, we demonstrate that many solutions to popular graph mining problems are highly dependent on the representation choice, thus calling current graph curation practices into question. As an homage to our data source, and asserting that science can also be art, we present our points in the form of a play.
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1.280
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 2055-7671
e-ISSN 2055-768X
Quellenangaben Band: 39, Heft: 1, Seiten: 74-96 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540003-001
Scopus ID 85189291902
Erfassungsdatum 2024-04-25