Hypercore decomposition for non-fragile hyperedges: concepts, algorithms, observations, and applications

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dc.contributor.authorBu, Fanchenko
dc.contributor.authorLee, Geonko
dc.contributor.authorShin, Kijungko
dc.date.accessioned2023-10-25T01:00:46Z-
dc.date.available2023-10-25T01:00:46Z-
dc.date.created2023-08-22-
dc.date.created2023-08-22-
dc.date.issued2023-11-
dc.identifier.citationDATA MINING AND KNOWLEDGE DISCOVERY, v.37, no.6, pp.2389 - 2437-
dc.identifier.issn1384-5810-
dc.identifier.urihttp://hdl.handle.net/10203/313761-
dc.description.abstractHypergraphs are a powerful abstraction for modeling high-order relations, which are ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (i.e., subsets of nodes); and there have been a number of attempts to extend the notion of k-cores, which proved useful with numerous applications for pairwise graphs, to hypergraphs. However, the previous extensions are based on an unrealistic assumption that hyperedges are fragile, i.e., a high-order relation becomes obsolete as soon as a single member leaves it.In this work, we propose a new substructure model, called (k, t)-hypercore, based on the assumption that high-order relations remain as long as at least t fraction of the members remains. Specifically, it is defined as the maximal subhypergraph where (1) every node is contained in at least k hyperedges in it and (2) at least t fraction of the nodes remain in every hyperedge. We first prove that, given t (or k ), finding the (k,t)-hypercore for every possible k (or t ) can be computed in time linear w.r.t the sum of the sizes of hyperedges. Then, we demonstrate that real-world hypergraphs from the same domain share similar (k, t)-hypercore structures, which capture different perspectives depending on t. Lastly, we show the successful applications of our model in identifying influential nodes, dense substructures, and vulnerability in hypergraphs.-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.titleHypercore decomposition for non-fragile hyperedges: concepts, algorithms, observations, and applications-
dc.typeArticle-
dc.identifier.wosid001044721800001-
dc.identifier.scopusid2-s2.0-85167347613-
dc.type.rimsART-
dc.citation.volume37-
dc.citation.issue6-
dc.citation.beginningpage2389-
dc.citation.endingpage2437-
dc.citation.publicationnameDATA MINING AND KNOWLEDGE DISCOVERY-
dc.identifier.doi10.1007/s10618-023-00956-2-
dc.contributor.localauthorShin, Kijung-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorHypergraphs mining-
dc.subject.keywordAuthork-cores-
dc.subject.keywordAuthorCohesive substructure models-
dc.subject.keywordAuthorReal-world hypergraph analysis-
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