All the world's a (hyper)graph: A data drama.
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 hypere...
| Publicado en: | Digital Scholarship in the Humanities Vol. 39; no. 1; pp. 74 - 97 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=176806329&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 176806329 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Apr2024 vid: 39 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 176806329 10.1093/llc/fqad071 ppf: 74 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P size: 3.1MB tig: atl: All the world's a (hyper)graph: A data drama. aug: au: Coupette, Corinna Vreeken, Jilles Rieck, Bastian affil: Max Planck Institute for Informatics , Saarbrücken, Germany CISPA Helmholtz Center for Information Security , Saarbrücken, Germany Institute of AI for Health, Helmholtz Munich , Munich, Germany su: Shakespeare, William, 1564-1616 Data release Hypergraphs sug: subj: Shakespeare, William, 1564-1616 Data release Hypergraphs ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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