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Trabalho de evento
TransGNN: a transductive graph neural network with graph dynamic embedding (2023)
Anghinoni, Leandro ; Yu-Tao, Zhu; Donghong, Ji; Liang, Zhao
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ANGHINONI, Leandro et al. TransGNN: a transductive graph neural network with graph dynamic embedding. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191134. Acesso em: 13 jun. 2024.
APA
Anghinoni, L., Yu-Tao, Z., Donghong, J., & Liang, Z. (2023). TransGNN: a transductive graph neural network with graph dynamic embedding. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191134
NLM
Anghinoni L, Yu-Tao Z, Donghong J, Liang Z. TransGNN: a transductive graph neural network with graph dynamic embedding [Internet]. Proceedings. 2023 ;[citado 2024 jun. 13 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191134
Vancouver
Anghinoni L, Yu-Tao Z, Donghong J, Liang Z. TransGNN: a transductive graph neural network with graph dynamic embedding [Internet]. Proceedings. 2023 ;[citado 2024 jun. 13 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191134
Artigo de periodico
Characterizing data patterns with core-periphery network modeling (2023)
Jianglong, Yan; Anghinoni, Leandro ; Yu-Tao, Zhu; Weiguang, Liu; Gen, Li; Qiusheng, Zheng; Liang, Zhao
Source: Journal of Computational Science. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, RADIOGRAFIA, COVID-19
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
JIANGLONG, Yan et al. Characterizing data patterns with core-periphery network modeling. Journal of Computational Science, v. 66, n. Ja 2023, p. 1-13, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.jocs.2022.101912. Acesso em: 13 jun. 2024.
APA
Jianglong, Y., Anghinoni, L., Yu-Tao, Z., Weiguang, L., Gen, L., Qiusheng, Z., & Liang, Z. (2023). Characterizing data patterns with core-periphery network modeling. Journal of Computational Science, 66( Ja 2023), 1-13. doi:10.1016/j.jocs.2022.101912
NLM
Jianglong Y, Anghinoni L, Yu-Tao Z, Weiguang L, Gen L, Qiusheng Z, Liang Z. Characterizing data patterns with core-periphery network modeling [Internet]. Journal of Computational Science. 2023 ; 66( Ja 2023): 1-13.[citado 2024 jun. 13 ] Available from: https://doi.org/10.1016/j.jocs.2022.101912
Vancouver
Jianglong Y, Anghinoni L, Yu-Tao Z, Weiguang L, Gen L, Qiusheng Z, Liang Z. Characterizing data patterns with core-periphery network modeling [Internet]. Journal of Computational Science. 2023 ; 66( Ja 2023): 1-13.[citado 2024 jun. 13 ] Available from: https://doi.org/10.1016/j.jocs.2022.101912
Artigo de periodico
Temporal network pattern identification by community modelling (2020)
Gao, Xubo; Zheng, Qiusheng; Vega-Oliveros, Didier Augusto ; Anghinoni, Leandro ; Liang, Zhao
Source: Scientific Reports. Unidades: FFCLRP, ICMC
Subjects: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GAO, Xubo et al. Temporal network pattern identification by community modelling. Scientific Reports, v. 10, p. 1-12, 2020Tradução . . Disponível em: https://doi.org/10.1038/s41598-019-57123-1. Acesso em: 13 jun. 2024.
APA
Gao, X., Zheng, Q., Vega-Oliveros, D. A., Anghinoni, L., & Liang, Z. (2020). Temporal network pattern identification by community modelling. Scientific Reports, 10, 1-12. doi:10.1038/s41598-019-57123-1
NLM
Gao X, Zheng Q, Vega-Oliveros DA, Anghinoni L, Liang Z. Temporal network pattern identification by community modelling [Internet]. Scientific Reports. 2020 ; 10 1-12.[citado 2024 jun. 13 ] Available from: https://doi.org/10.1038/s41598-019-57123-1
Vancouver
Gao X, Zheng Q, Vega-Oliveros DA, Anghinoni L, Liang Z. Temporal network pattern identification by community modelling [Internet]. Scientific Reports. 2020 ; 10 1-12.[citado 2024 jun. 13 ] Available from: https://doi.org/10.1038/s41598-019-57123-1
Trabalho de evento
Time series trend detection and forecasting using complex network topology analysis (2018)
Anghinoni, Leandro; Liang, Zhao ; Zheng, Qiusheng; Zhang, Junbao
Source: Annals. Conference titles: International Joint Conference on Neural Networks (IJCNN). Unidade: FFCLRP
Assunto: REDES COMPLEXAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ANGHINONI, Leandro et al. Time series trend detection and forecasting using complex network topology analysis. 2018, Anais.. Rio de Janeiro: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2018. Disponível em: https://doi.org/10.1109/ijcnn.2018.8489167. Acesso em: 13 jun. 2024.
APA
Anghinoni, L., Liang, Z., Zheng, Q., & Zhang, J. (2018). Time series trend detection and forecasting using complex network topology analysis. In Annals. Rio de Janeiro: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1109/ijcnn.2018.8489167
NLM
Anghinoni L, Liang Z, Zheng Q, Zhang J. Time series trend detection and forecasting using complex network topology analysis [Internet]. Annals. 2018 ;[citado 2024 jun. 13 ] Available from: https://doi.org/10.1109/ijcnn.2018.8489167
Vancouver
Anghinoni L, Liang Z, Zheng Q, Zhang J. Time series trend detection and forecasting using complex network topology analysis [Internet]. Annals. 2018 ;[citado 2024 jun. 13 ] Available from: https://doi.org/10.1109/ijcnn.2018.8489167

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