How Much and When Do We Need Higher-order Informationin Hypergraphs? A Case Study on Hyperedge Prediction

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 91
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorYoon, Se-eunko
dc.contributor.authorSong, Hyungseokko
dc.contributor.authorShin, Kijungko
dc.contributor.authorYi, Yungko
dc.date.accessioned2020-06-29T08:20:30Z-
dc.date.available2020-06-29T08:20:30Z-
dc.date.created2020-06-28-
dc.date.created2020-06-28-
dc.date.issued2020-04-20-
dc.identifier.citationThe Web Conference 2020, WWW 2020, pp.2627 - 2633-
dc.identifier.urihttp://hdl.handle.net/10203/275004-
dc.description.abstractHypergraphs provide a natural way of representing group relations, whose complexity motivates an extensive array of prior work to adopt some form of abstraction and simplification of higher-order interactions. However, the following question has yet to be addressed: How much abstraction of group interactions is sufficient in solving a hypergraph task, and how different such results become across datasets? This question, if properly answered, provides a useful engineering guideline on how to trade off between complexity and accuracy of solving a downstream task. To this end, we propose a method of incrementally representing group interactions using a notion of n-projected graph whose accumulation contains information on up to n-way interactions, and quantify the accuracy of solving a task as n grows for various datasets. As a downstream task, we consider hyperedge prediction, an extension of link prediction, which is a canonical task for evaluating graph models. Through experiments on 15 real-world datasets, we draw the following messages: (a) Diminishing returns: small n is enough to achieve accuracy comparable with near-perfect approximations, (b) Troubleshooter: as the task becomes more challenging, larger n brings more benefit, and (c) Irreducibility: datasets whose pairwise interactions do not tell much about higher-order interactions lose much accuracy when reduced to pairwise abstractions.-
dc.languageEnglish-
dc.publisherAssociation for Computing Machinery-
dc.titleHow Much and When Do We Need Higher-order Informationin Hypergraphs? A Case Study on Hyperedge Prediction-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.beginningpage2627-
dc.citation.endingpage2633-
dc.citation.publicationnameThe Web Conference 2020, WWW 2020-
dc.identifier.conferencecountryCH-
dc.identifier.conferencelocationTaipei Taiwan-
dc.identifier.doi10.1145/3366423.3380016-
dc.contributor.localauthorShin, Kijung-
dc.contributor.localauthorYi, Yung-
dc.contributor.nonIdAuthorYoon, Se-eun-
dc.contributor.nonIdAuthorSong, Hyungseok-
Appears in Collection
RIMS Conference PapersEE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0