One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication (2025)
- Authors:
- USP affiliated authors: SILVA, DIEGO FURTADO - ICMC ; MARCACINI, RICARDO MARCONDES - ICMC ; GÔLO, MARCOS PAULO SILVA - ICMC ; MEDEIROS JÚNIOR, JOSÉ GILBERTO BARBOSA DE - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.ins.2025.122060
- Subjects: APRENDIZADO COMPUTACIONAL; REDES NEURAIS; TEORIA DOS GRAFOS
- Keywords: One-class learning; Graph neural networks; Graph learning; Interpretability
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Information Sciences
- ISSN: 0020-0255
- Volume/Número/Paginação/Ano: v. 708, p. 1-17, Aug. 2025
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
GÔLO, Marcos Paulo Silva et al. One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication. Information Sciences, v. 708, p. 1-17, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2025.122060. Acesso em: 26 dez. 2025. -
APA
Gôlo, M. P. S., Medeiros Júnior, J. G. B. de, Silva, D. F., & Marcacini, R. M. (2025). One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication. Information Sciences, 708, 1-17. doi:10.1016/j.ins.2025.122060 -
NLM
Gôlo MPS, Medeiros Júnior JGB de, Silva DF, Marcacini RM. One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication [Internet]. Information Sciences. 2025 ; 708 1-17.[citado 2025 dez. 26 ] Available from: https://doi.org/10.1016/j.ins.2025.122060 -
Vancouver
Gôlo MPS, Medeiros Júnior JGB de, Silva DF, Marcacini RM. One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication [Internet]. Information Sciences. 2025 ; 708 1-17.[citado 2025 dez. 26 ] Available from: https://doi.org/10.1016/j.ins.2025.122060 - Large language models ensemble for biochemical properties detection in scientific articles
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Informações sobre o DOI: 10.1016/j.ins.2025.122060 (Fonte: oaDOI API)
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