{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/all-the-world-s-a-hyper-graph-a-data-drama","title":"All the World's a (Hyper)Graph: A Data Drama","arxiv_id":"2206.08225","date":"2022-06-16","proceeding":null,"authors":["Corinna Coupette","Jilles Vreeken","Bastian Rieck"],"abstract":"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 all our points in the form of a play.","url_abs":"https://arxiv.org/abs/2206.08225v3","url_pdf":"https://arxiv.org/pdf/2206.08225v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"all-the-world-s-a-hyper-graph-a-data-drama","repo_url":"https://github.com/hyperbard/hyperbard","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"all-the-world-s-a-hyper-graph-a-data-drama","repo_url":"https://github.com/hyperbard/tutorials","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"graph-mining","task_name":"Graph Mining"}],"methods":[],"datasets_introduced":[{"slug":"hyperbard","name":"Hyperbard","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.08225","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.08225"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/hyperbard/hyperbard","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/hyperbard/tutorials","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"8fb3f5471212c1dd","entry":"edge_dataframe","repo":"hyperbard/hyperbard","repo_kind":"official","path":"src/hyperbard/create_graph_representations.py","file_url":"https://github.com/hyperbard/hyperbard/blob/HEAD/src/hyperbard/create_graph_representations.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"8fb3f5471212c1dd"}},{"code_sha256_prefix":"b78615d5b7f1e176","entry":"node_dataframe","repo":"hyperbard/hyperbard","repo_kind":"official","path":"src/hyperbard/create_graph_representations.py","file_url":"https://github.com/hyperbard/hyperbard/blob/HEAD/src/hyperbard/create_graph_representations.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"b78615d5b7f1e176"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}