RaDE+: A semantic rank-based graph embedding algorithm

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorde Fernando, Filipe Alves-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Autor(es): dc.creatorde Sousa, Gustavo José-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorGuilherme, Ivan Rizzo-
Data de aceite: dc.date.accessioned2025-08-21T20:14:18Z-
Data de disponibilização: dc.date.available2025-08-21T20:14:18Z-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2022-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.jjimei.2022.100078-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241880-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241880-
Descrição: dc.descriptionDue to the possibility of capturing complex relationships existing between nodes, many applications benefit from being modeled with graphs. However, performance issues can be observed in large-scale networks, making it computationally unfeasible to process in various scenarios. Graph Embedding methods emerge as a promising solution for finding low-dimensional vector representations for graphs, preserving their original properties such as topological characteristics, affinity, and shared neighborhood between nodes. Based on the vectorial representations, 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 effective and efficient approach that considers rank-based graphs and representative nodes selection for learning a low-dimensional vector. We also present RaDE+, a variant that considers multiple representative nodes for more robust representations. The proposed approach was evaluated on 8 network datasets, including 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.descriptionUNESP: Universidade Estadual Paulista Julio de Mesquita Filho Limeira-
Descrição: dc.descriptionUNESP: Universidade Estadual Paulista Julio de Mesquita Filho Limeira-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Information Management Data Insights-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDiffusion-
Palavras-chave: dc.subjectGraph embedding-
Palavras-chave: dc.subjectInterpretability-
Palavras-chave: dc.subjectNetwork representation learning-
Palavras-chave: dc.subjectRanking-
Palavras-chave: dc.subjectSemantic-
Palavras-chave: dc.subjectUnsupervised-
Título: dc.titleRaDE+: A semantic rank-based graph embedding algorithm-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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