DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Se-eun | ko |
dc.contributor.author | Song, Hyungseok | ko |
dc.contributor.author | Shin, Kijung | ko |
dc.contributor.author | Yi, Yung | ko |
dc.date.accessioned | 2020-06-29T08:20:30Z | - |
dc.date.available | 2020-06-29T08:20:30Z | - |
dc.date.created | 2020-06-28 | - |
dc.date.created | 2020-06-28 | - |
dc.date.created | 2020-06-28 | - |
dc.date.issued | 2020-04-20 | - |
dc.identifier.citation | The Web Conference 2020, WWW 2020, pp.2627 - 2633 | - |
dc.identifier.uri | http://hdl.handle.net/10203/275004 | - |
dc.description.abstract | Hypergraphs 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.language | English | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction | - |
dc.type | Conference | - |
dc.identifier.wosid | 000626273302073 | - |
dc.identifier.scopusid | 2-s2.0-85086567263 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 2627 | - |
dc.citation.endingpage | 2633 | - |
dc.citation.publicationname | The Web Conference 2020, WWW 2020 | - |
dc.identifier.conferencecountry | CH | - |
dc.identifier.conferencelocation | Taipei Taiwan | - |
dc.identifier.doi | 10.1145/3366423.3380016 | - |
dc.contributor.localauthor | Shin, Kijung | - |
dc.contributor.localauthor | Yi, Yung | - |
dc.contributor.nonIdAuthor | Yoon, Se-eun | - |
dc.contributor.nonIdAuthor | Song, Hyungseok | - |
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