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Artigo de periodico
ZigzagNetVis: suggesting temporal resolutions for graph visualization using zigzag persistence (2025)
Tinarrage, Raphaël ; Ponciano, Jean Roberto ; Linhares, Claudio Douglas Gouveia ; Traina, Agma Juci Machado ; Poco, Jorge
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: VISUALIZAÇÃO, MATEMÁTICA DA COMPUTAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TINARRAGE, Raphaël et al. ZigzagNetVis: suggesting temporal resolutions for graph visualization using zigzag persistence. IEEE Transactions on Visualization and Computer Graphics, v. 31, n. 10, p. 6852-6869, 2025Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2025.3528197. Acesso em: 27 nov. 2025.
APA
Tinarrage, R., Ponciano, J. R., Linhares, C. D. G., Traina, A. J. M., & Poco, J. (2025). ZigzagNetVis: suggesting temporal resolutions for graph visualization using zigzag persistence. IEEE Transactions on Visualization and Computer Graphics, 31( 10), 6852-6869. doi:10.1109/TVCG.2025.3528197
NLM
Tinarrage R, Ponciano JR, Linhares CDG, Traina AJM, Poco J. ZigzagNetVis: suggesting temporal resolutions for graph visualization using zigzag persistence [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10): 6852-6869.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/TVCG.2025.3528197
Vancouver
Tinarrage R, Ponciano JR, Linhares CDG, Traina AJM, Poco J. ZigzagNetVis: suggesting temporal resolutions for graph visualization using zigzag persistence [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10): 6852-6869.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/TVCG.2025.3528197
Trabalho de evento
Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences (2025)
Alam, Md Mahbub ; Soares, Amilcar ; Rodrigues Junior, José Fernando ; Spadon, Gabriel
Source: Proceedings. Conference titles: International Symposium on Spatial and Temporal Data - SSTD. Unidade: ICMC
Subjects: REDES NEURAIS, NAVEGAÇÃO MARÍTIMA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALAM, Md Mahbub et al. Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences. 2025, Anais.. New York: ACM, 2025. Disponível em: https://doi.org/10.1145/3748777.3748779. Acesso em: 27 nov. 2025.
APA
Alam, M. M., Soares, A., Rodrigues Junior, J. F., & Spadon, G. (2025). Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences. In Proceedings. New York: ACM. doi:10.1145/3748777.3748779
NLM
Alam MM, Soares A, Rodrigues Junior JF, Spadon G. Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences [Internet]. Proceedings. 2025 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1145/3748777.3748779
Vancouver
Alam MM, Soares A, Rodrigues Junior JF, Spadon G. Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences [Internet]. Proceedings. 2025 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1145/3748777.3748779

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