Source: Information Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, TEORIA DOS GRAFOS
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: 23 maio 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.2025.122060NLM
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 maio 23 ] Available from: https://doi.org/10.1016/j.ins.2025.122060Vancouver
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 maio 23 ] Available from: https://doi.org/10.1016/j.ins.2025.122060