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All the world's a (hyper)graph: A data drama.
Digit. Scholarsh. Humanit. 39, 74-96 (2024)
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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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
ISSN (print) / ISBN
2055-7671
e-ISSN
2055-768X
Zeitschrift
Digital Scholarship in the Humanities
Quellenangaben
Band: 39,
Heft: 1,
Seiten: 74-96
Verlag
Oxford University Press
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of AI for Health (AIH)