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Filtros : "TEORIA DOS GRAFOS" "FUJITA, ANDRÉ" "IME" Removidos: "1967" "IP" Limpar

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  • Artigo de periodico

    Spectral density-based clustering algorithms for complex networks (2023)

    Ramos, Taiane Coelho ; Mourão-Miranda, Janaina ; Fujita, André

    Source: Frontiers in Neuroscience. Unidades: IME, ICMC

    Subjects: REDES COMPLEXAS, TEORIA DOS GRAFOS, TEORIA ESPECTRAL

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    • ABNT

      RAMOS, Taiane Coelho e MOURÃO-MIRANDA, Janaina e FUJITA, André. Spectral density-based clustering algorithms for complex networks. Frontiers in Neuroscience, v. 17, p. 1-14, 2023Tradução . . Disponível em: https://doi.org/10.3389/fnins.2023.926321. Acesso em: 16 nov. 2024.

    • APA

      Ramos, T. C., Mourão-Miranda, J., & Fujita, A. (2023). Spectral density-based clustering algorithms for complex networks. Frontiers in Neuroscience, 17, 1-14. doi:10.3389/fnins.2023.926321

    • NLM

      Ramos TC, Mourão-Miranda J, Fujita A. Spectral density-based clustering algorithms for complex networks [Internet]. Frontiers in Neuroscience. 2023 ; 17 1-14.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3389/fnins.2023.926321

    • Vancouver

      Ramos TC, Mourão-Miranda J, Fujita A. Spectral density-based clustering algorithms for complex networks [Internet]. Frontiers in Neuroscience. 2023 ; 17 1-14.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3389/fnins.2023.926321

  • Artigo de periodico

    Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms (2022)

    Oku, Amanda Yumi Ambriola ; Barreto, Candida ; Bruneri, Guilherme; Brockington, Guilherme ; Fujita, André ; Sato, João Ricardo

    Source: Frontiers in Computational Neuroscience. Unidade: IME

    Subjects: TEORIA DOS GRAFOS, NEUROCIÊNCIAS

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      OKU, Amanda Yumi Ambriola et al. Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms. Frontiers in Computational Neuroscience, v. 16, 2022Tradução . . Disponível em: https://doi.org/10.3389/fncom.2022.975743. Acesso em: 16 nov. 2024.

    • APA

      Oku, A. Y. A., Barreto, C., Bruneri, G., Brockington, G., Fujita, A., & Sato, J. R. (2022). Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms. Frontiers in Computational Neuroscience, 16. doi:10.3389/fncom.2022.975743

    • NLM

      Oku AYA, Barreto C, Bruneri G, Brockington G, Fujita A, Sato JR. Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms [Internet]. Frontiers in Computational Neuroscience. 2022 ; 16[citado 2024 nov. 16 ] Available from: https://doi.org/10.3389/fncom.2022.975743

    • Vancouver

      Oku AYA, Barreto C, Bruneri G, Brockington G, Fujita A, Sato JR. Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms [Internet]. Frontiers in Computational Neuroscience. 2022 ; 16[citado 2024 nov. 16 ] Available from: https://doi.org/10.3389/fncom.2022.975743

  • Artigo de periodico

    A fast parameter estimator for large complex networks (2022)

    Guzman, Grover Enrique Castro ; Takahashi, Daniel Yasumasa; Fujita, André

    Source: Journal of Complex Networks. Unidade: IME

    Subjects: COMBINATÓRIA, TEORIA DOS GRAFOS

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      GUZMAN, Grover Enrique Castro e TAKAHASHI, Daniel Yasumasa e FUJITA, André. A fast parameter estimator for large complex networks. Journal of Complex Networks, v. 10, n. artigo cnac022. p. 1-11, 2022Tradução . . Disponível em: https://doi.org/10.1093/comnet/cnac022. Acesso em: 16 nov. 2024.

    • APA

      Guzman, G. E. C., Takahashi, D. Y., & Fujita, A. (2022). A fast parameter estimator for large complex networks. Journal of Complex Networks, 10( artigo cnac022. p. 1-11). doi:10.1093/comnet/cnac022

    • NLM

      Guzman GEC, Takahashi DY, Fujita A. A fast parameter estimator for large complex networks [Internet]. Journal of Complex Networks. 2022 ; 10( artigo cnac022. p. 1-11):[citado 2024 nov. 16 ] Available from: https://doi.org/10.1093/comnet/cnac022

    • Vancouver

      Guzman GEC, Takahashi DY, Fujita A. A fast parameter estimator for large complex networks [Internet]. Journal of Complex Networks. 2022 ; 10( artigo cnac022. p. 1-11):[citado 2024 nov. 16 ] Available from: https://doi.org/10.1093/comnet/cnac022

  • Artigo de periodico

    Efficient eigenvalue counts for tree-like networks (2022)

    Guzman, Grover Enrique Castro ; Stadler, Peter F. ; Fujita, André

    Source: Journal of Complex Networks. Unidade: IME

    Subjects: COMBINATÓRIA, TEORIA DOS GRAFOS

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      GUZMAN, Grover Enrique Castro e STADLER, Peter F. e FUJITA, André. Efficient eigenvalue counts for tree-like networks. Journal of Complex Networks, v. 10, n. 5, 2022Tradução . . Disponível em: https://doi.org/10.1093/comnet/cnac040. Acesso em: 16 nov. 2024.

    • APA

      Guzman, G. E. C., Stadler, P. F., & Fujita, A. (2022). Efficient eigenvalue counts for tree-like networks. Journal of Complex Networks, 10( 5). doi:10.1093/comnet/cnac040

    • NLM

      Guzman GEC, Stadler PF, Fujita A. Efficient eigenvalue counts for tree-like networks [Internet]. Journal of Complex Networks. 2022 ; 10( 5):[citado 2024 nov. 16 ] Available from: https://doi.org/10.1093/comnet/cnac040

    • Vancouver

      Guzman GEC, Stadler PF, Fujita A. Efficient eigenvalue counts for tree-like networks [Internet]. Journal of Complex Networks. 2022 ; 10( 5):[citado 2024 nov. 16 ] Available from: https://doi.org/10.1093/comnet/cnac040

  • Artigo de periodico

    Spectral density of random graphs: convergence properties and application in model fitting (2021)

    Santos, Suzana de Siqueira ; Fujita, André ; Matias, Catherine

    Source: Journal of Complex Networks. Unidade: IME

    Assunto: TEORIA DOS GRAFOS

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      SANTOS, Suzana de Siqueira e FUJITA, André e MATIAS, Catherine. Spectral density of random graphs: convergence properties and application in model fitting. Journal of Complex Networks, v. 9, n. 6, p. 1-27, 2021Tradução . . Disponível em: https://doi.org/10.1093/comnet/cnab041. Acesso em: 16 nov. 2024.

    • APA

      Santos, S. de S., Fujita, A., & Matias, C. (2021). Spectral density of random graphs: convergence properties and application in model fitting. Journal of Complex Networks, 9( 6), 1-27. doi:10.1093/comnet/cnab041

    • NLM

      Santos S de S, Fujita A, Matias C. Spectral density of random graphs: convergence properties and application in model fitting [Internet]. Journal of Complex Networks. 2021 ; 9( 6): 1-27.[citado 2024 nov. 16 ] Available from: https://doi.org/10.1093/comnet/cnab041

    • Vancouver

      Santos S de S, Fujita A, Matias C. Spectral density of random graphs: convergence properties and application in model fitting [Internet]. Journal of Complex Networks. 2021 ; 9( 6): 1-27.[citado 2024 nov. 16 ] Available from: https://doi.org/10.1093/comnet/cnab041

  • Artigo de periodico

    Efficient Laplacian spectral density computations for networks with arbitrary degree distributions (2021)

    Guzman, Grover Enrique Castro ; Stadler, Peter F. ; Fujita, André

    Source: Network Science. Unidade: IME

    Assunto: TEORIA DOS GRAFOS

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      GUZMAN, Grover Enrique Castro e STADLER, Peter F. e FUJITA, André. Efficient Laplacian spectral density computations for networks with arbitrary degree distributions. Network Science, v. 9, n. 3, p. 312-327, 2021Tradução . . Disponível em: https://doi.org/10.1017/nws.2021.10. Acesso em: 16 nov. 2024.

    • APA

      Guzman, G. E. C., Stadler, P. F., & Fujita, A. (2021). Efficient Laplacian spectral density computations for networks with arbitrary degree distributions. Network Science, 9( 3), 312-327. doi:10.1017/nws.2021.10

    • NLM

      Guzman GEC, Stadler PF, Fujita A. Efficient Laplacian spectral density computations for networks with arbitrary degree distributions [Internet]. Network Science. 2021 ; 9( 3): 312-327.[citado 2024 nov. 16 ] Available from: https://doi.org/10.1017/nws.2021.10

    • Vancouver

      Guzman GEC, Stadler PF, Fujita A. Efficient Laplacian spectral density computations for networks with arbitrary degree distributions [Internet]. Network Science. 2021 ; 9( 3): 312-327.[citado 2024 nov. 16 ] Available from: https://doi.org/10.1017/nws.2021.10

  • Artigo de periodico

    Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory (2020)

    Oku, Amanda Yumi Ambriola ; Morais, Guilherme Augusto Zimeo ; Bueno, Ana Paula Arantes ; Fujita, André ; Sato, João Ricardo

    Source: International Journal of Environmental Research and Public Health. Unidade: IME

    Subjects: ADOLESCENTES, TEORIA DOS GRAFOS

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      OKU, Amanda Yumi Ambriola et al. Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory. International Journal of Environmental Research and Public Health, v. 17, n. 1, p. 1-10, 2020Tradução . . Disponível em: https://doi.org/10.3390/ijerph17010090. Acesso em: 16 nov. 2024.

    • APA

      Oku, A. Y. A., Morais, G. A. Z., Bueno, A. P. A., Fujita, A., & Sato, J. R. (2020). Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory. International Journal of Environmental Research and Public Health, 17( 1), 1-10. doi:10.3390/ijerph17010090

    • NLM

      Oku AYA, Morais GAZ, Bueno APA, Fujita A, Sato JR. Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory [Internet]. International Journal of Environmental Research and Public Health. 2020 ; 17( 1): 1-10.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/ijerph17010090

    • Vancouver

      Oku AYA, Morais GAZ, Bueno APA, Fujita A, Sato JR. Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory [Internet]. International Journal of Environmental Research and Public Health. 2020 ; 17( 1): 1-10.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/ijerph17010090

  • Parte de monografia/livro

    Statistical methods in graphs: parameter estimation, model selection, and hypothesis test (2016)

    Santos, Suzana de Siqueira; Takahashi, Daniel Yasumasa; Sato, João Ricardo; Ferreira, Carlos Eduardo ; Fujita, André

    Source: Mathematical foundations and applications of graph entropy. Unidade: IME

    Subjects: TEORIA DOS GRAFOS, PROBABILIDADE, ESTIMAÇÃO PARAMÉTRICA, TESTES DE HIPÓTESES, SELEÇÃO DE MODELOS, BIOESTATÍSTICA

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      SANTOS, Suzana de Siqueira et al. Statistical methods in graphs: parameter estimation, model selection, and hypothesis test. Mathematical foundations and applications of graph entropy. Tradução . Weinheim: Wiley-VCH, 2016. . Disponível em: https://doi.org/10.1002/9783527693245.ch6. Acesso em: 16 nov. 2024.

    • APA

      Santos, S. de S., Takahashi, D. Y., Sato, J. R., Ferreira, C. E., & Fujita, A. (2016). Statistical methods in graphs: parameter estimation, model selection, and hypothesis test. In Mathematical foundations and applications of graph entropy. Weinheim: Wiley-VCH. doi:10.1002/9783527693245.ch6

    • NLM

      Santos S de S, Takahashi DY, Sato JR, Ferreira CE, Fujita A. Statistical methods in graphs: parameter estimation, model selection, and hypothesis test [Internet]. In: Mathematical foundations and applications of graph entropy. Weinheim: Wiley-VCH; 2016. [citado 2024 nov. 16 ] Available from: https://doi.org/10.1002/9783527693245.ch6

    • Vancouver

      Santos S de S, Takahashi DY, Sato JR, Ferreira CE, Fujita A. Statistical methods in graphs: parameter estimation, model selection, and hypothesis test [Internet]. In: Mathematical foundations and applications of graph entropy. Weinheim: Wiley-VCH; 2016. [citado 2024 nov. 16 ] Available from: https://doi.org/10.1002/9783527693245.ch6
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