DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yi, Yung | - |
dc.contributor.advisor | 이융 | - |
dc.contributor.advisor | Shin, Kijung | - |
dc.contributor.advisor | 신기정 | - |
dc.contributor.author | Yoon, Se-eun | - |
dc.date.accessioned | 2021-05-13T19:39:28Z | - |
dc.date.available | 2021-05-13T19:39:28Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925228&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/285064 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2020.8,[iii, 27 p. :] | - |
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 | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | hypergraphs▼ahyperedge prediction▼alink prediction▼agraph mining▼adata mining | - |
dc.subject | 하이퍼그래프▼a하이퍼엣지 예측▼a링크 예측▼a그래프 마이닝▼a데이터 마이닝 | - |
dc.title | How much and when do we need higher-order information in hypergraphs? A Case Study on Hyperedge Prediction | - |
dc.title.alternative | 하이퍼그래프에서 고차 정보는 언제 얼마나 필요할까? 하이퍼엣지 예측에서의 사례 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 윤세은 | - |
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