RaDE: A Rank-based Graph Embedding Approach

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorFernando, Filipe Alves de [UNESP]-
Autor(es): dc.creatorGuimaraes Pedronette, Daniel Carlos [UNESP]-
Autor(es): dc.creatorSousa, Gustavo Jose de [UNESP]-
Autor(es): dc.creatorValem, Lucas Pascotti [UNESP]-
Autor(es): dc.creatorGuilherme, Ivan Rizzo [UNESP]-
Autor(es): dc.creatorFarinella, G. M.-
Autor(es): dc.creatorRadeva, P.-
Autor(es): dc.creatorBraz, J.-
Data de aceite: dc.date.accessioned2022-02-22T01:01:06Z-
Data de disponibilização: dc.date.available2022-02-22T01:01:06Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0008985901420152-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/210488-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/210488-
Descrição: dc.descriptionDue to possibility of capturing complex relationships existing between nodes, many application benefit of being modeled with graphs. However, performance issues can be observed on large scale networks, making it computationally unfeasible to process information in various scenarios. Graph Embedding methods are usually used for finding low-dimensional vector representations for graphs, preserving its original properties such as topological characteristics, affinity and shared neighborhood between nodes. In this way, retrieval and machine learning techniques can be exploited to execute tasks such as classification, clustering, and link prediction. In this work, we propose RaDE (Rank Diffusion Embedding), an efficient and effective approach that considers rank-based graphs for learning a low-dimensional vector. The proposed approach was evaluated on 7 network datasets such as a social, co-reference, textual and image networks, with different properties. Vector representations generated with RaDE achieved effective results in visualization and retrieval tasks when compared to vector representations generated by other recent related methods.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionPetrobras-
Descrição: dc.descriptionUNESP Sao Paulo State Univ, Inst Geosci & Exact Sci, Rio Claro, SP, Brazil-
Descrição: dc.descriptionUNESP Sao Paulo State Univ, Inst Geosci & Exact Sci, Rio Claro, SP, Brazil-
Descrição: dc.descriptionFAPESP: 2017/25908-6-
Descrição: dc.descriptionFAPESP: 2018/15597-6-
Descrição: dc.descriptionCNPq: 308194/2017-9-
Descrição: dc.descriptionPetrobras: 2014/00545-0-
Descrição: dc.descriptionPetrobras: 2017/00285-6-
Formato: dc.format142-152-
Idioma: dc.languageen-
Publicador: dc.publisherScitepress-
Relação: dc.relationProceedings Of The 15th International Joint Conference On Computer Vision, Imaging And Computer Graphics Theory And Applications, Vol 5: Visapp-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectRaDE-
Palavras-chave: dc.subjectGraph Embedding-
Palavras-chave: dc.subjectNetwork Representation Learning-
Palavras-chave: dc.subjectRanking-
Título: dc.titleRaDE: A Rank-based Graph Embedding Approach-
Aparece nas coleções:Repositório Institucional - Unesp

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