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Artigo de periodico
Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks (2024)
Valejo, Alan Demetrius Baria ; Althoff, Paulo Eduardo ; Faleiros, Thiago de Paulo ; Rocha Filho, Geraldo Pereira ; Yu-Tao, Zhu ; Jianglong, Yan; Weiguang, Liu; Liang, Zhao
Source: IEEE Transactions on Network Science and Engineering. Unidades: FFCLRP, ICMC
Subjects: APRENDIZADO COMPUTACIONAL, DESCOBERTA DE CONHECIMENTO, ALGORITMOS ÚTEIS E ESPECÍFICOS, REDES COMPLEXAS
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ABNT
VALEJO, Alan Demetrius Baria et al. Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks. IEEE Transactions on Network Science and Engineering, v. 11, n. 2, p. 1799-1809, 2024Tradução . . Disponível em: https://doi.org/10.1109/TNSE.2023.3331655. Acesso em: 30 ago. 2024.
APA
Valejo, A. D. B., Althoff, P. E., Faleiros, T. de P., Rocha Filho, G. P., Yu-Tao, Z., Jianglong, Y., et al. (2024). Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks. IEEE Transactions on Network Science and Engineering, 11( 2), 1799-1809. doi:10.1109/TNSE.2023.3331655
NLM
Valejo ADB, Althoff PE, Faleiros T de P, Rocha Filho GP, Yu-Tao Z, Jianglong Y, Weiguang L, Liang Z. Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks [Internet]. IEEE Transactions on Network Science and Engineering. 2024 ; 11( 2): 1799-1809.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1109/TNSE.2023.3331655
Vancouver
Valejo ADB, Althoff PE, Faleiros T de P, Rocha Filho GP, Yu-Tao Z, Jianglong Y, Weiguang L, Liang Z. Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks [Internet]. IEEE Transactions on Network Science and Engineering. 2024 ; 11( 2): 1799-1809.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1109/TNSE.2023.3331655
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
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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: 30 ago. 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 ago. 30 ] 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 ago. 30 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191134
Trabalho de evento
Complex network-based data classification using minimum spanning tree metric and optimization (2023)
Saire, Josimar Edinson Chire ; Liang, Zhao
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL
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SAIRE, Josimar Edinson Chire e LIANG, Zhao. Complex network-based data classification using minimum spanning tree metric and optimization. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191004. Acesso em: 30 ago. 2024.
APA
Saire, J. E. C., & Liang, Z. (2023). Complex network-based data classification using minimum spanning tree metric and optimization. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191004
NLM
Saire JEC, Liang Z. Complex network-based data classification using minimum spanning tree metric and optimization [Internet]. Proceedings. 2023 ;[citado 2024 ago. 30 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191004
Vancouver
Saire JEC, Liang Z. Complex network-based data classification using minimum spanning tree metric and optimization [Internet]. Proceedings. 2023 ;[citado 2024 ago. 30 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191004
Trabalho de evento
WSI2ML: an open-source whole slide image annotation software for machine learning applications (2023)
Martins, Luan Vinicius de Carvalho ; Bueno, Adriana Passos ; Defelicibus, Alexandre ; Drummond, Rodrigo Duarte ; Valieris, Renan ; Yu-Tao, Zhu ; Silva, Israel Tojal da ; Liang, Zhao
Source: Proceedings. Conference titles: Brazilian Symposium on Multimedia and the Web - WebMedia. Unidades: FFCLRP, ICMC
Subjects: BIOINFORMÁTICA, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, PATOLOGIA CLÍNICA, TECNOLOGIAS DA SAÚDE
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MARTINS, Luan Vinicius de Carvalho et al. WSI2ML: an open-source whole slide image annotation software for machine learning applications. 2023, Anais.. New York: ACM, 2023. Disponível em: https://doi.org/10.1145/3617023.3617038. Acesso em: 30 ago. 2024.
APA
Martins, L. V. de C., Bueno, A. P., Defelicibus, A., Drummond, R. D., Valieris, R., Yu-Tao, Z., et al. (2023). WSI2ML: an open-source whole slide image annotation software for machine learning applications. In Proceedings. New York: ACM. doi:10.1145/3617023.3617038
NLM
Martins LV de C, Bueno AP, Defelicibus A, Drummond RD, Valieris R, Yu-Tao Z, Silva IT da, Liang Z. WSI2ML: an open-source whole slide image annotation software for machine learning applications [Internet]. Proceedings. 2023 ;[citado 2024 ago. 30 ] Available from: https://doi.org/10.1145/3617023.3617038
Vancouver
Martins LV de C, Bueno AP, Defelicibus A, Drummond RD, Valieris R, Yu-Tao Z, Silva IT da, Liang Z. WSI2ML: an open-source whole slide image annotation software for machine learning applications [Internet]. Proceedings. 2023 ;[citado 2024 ago. 30 ] Available from: https://doi.org/10.1145/3617023.3617038
Tese (Doutorado)
Structure characterization of complex networks for machine learning (2023)
Anghinoni, Leandro ; Liang, Zhao (Orientador) ; Silva, Israel T. (coorient)
Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, REDES NEURAIS, ANÁLISE DE SÉRIES TEMPORAIS
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ANGHINONI, Leandro. Structure characterization of complex networks for machine learning. 2023. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092023-143213/. Acesso em: 30 ago. 2024.
APA
Anghinoni, L. (2023). Structure characterization of complex networks for machine learning (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092023-143213/
NLM
Anghinoni L. Structure characterization of complex networks for machine learning [Internet]. 2023 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092023-143213/
Vancouver
Anghinoni L. Structure characterization of complex networks for machine learning [Internet]. 2023 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092023-143213/
Tese (Doutorado)
A Modified Echo State Network Model Using Non-Random Topology (2023)
Arroyo, Diana Carolina Roca ; Liang, Zhao (Orientador) ; Silva, Israel T. (coorient)
Unidade: ICMC
Subjects: REDES NEURAIS, REDES COMPLEXAS, MINERAÇÃO DE DADOS, REGRESSÃO LINEAR
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ARROYO, Diana Carolina Roca. A Modified Echo State Network Model Using Non-Random Topology. 2023. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-12092023-210015/. Acesso em: 30 ago. 2024.
APA
Arroyo, D. C. R. (2023). A Modified Echo State Network Model Using Non-Random Topology (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-12092023-210015/
NLM
Arroyo DCR. A Modified Echo State Network Model Using Non-Random Topology [Internet]. 2023 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-12092023-210015/
Vancouver
Arroyo DCR. A Modified Echo State Network Model Using Non-Random Topology [Internet]. 2023 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-12092023-210015/
Tese (Doutorado)
Classificação de Alto Nível Baseada em Redes Complexas (2023)
Saire, Josimar Edinson Chire ; Liang, Zhao (Orientador)
Unidade: ICMC
Subjects: REDES COMPLEXAS, COLÔNIAS DE FORMIGAS, ALGORITMOS GENÉTICOS, APRENDIZADO COMPUTACIONAL, COVID-19
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ABNT
SAIRE, Josimar Edinson Chire. Classificação de Alto Nível Baseada em Redes Complexas. 2023. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02022024-172326/. Acesso em: 30 ago. 2024.
APA
Saire, J. E. C. (2023). Classificação de Alto Nível Baseada em Redes Complexas (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02022024-172326/
NLM
Saire JEC. Classificação de Alto Nível Baseada em Redes Complexas [Internet]. 2023 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02022024-172326/
Vancouver
Saire JEC. Classificação de Alto Nível Baseada em Redes Complexas [Internet]. 2023 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02022024-172326/
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
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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: 30 ago. 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 ago. 30 ] 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 ago. 30 ] Available from: https://doi.org/10.1016/j.jocs.2022.101912
Artigo de periodico
Complex network-based classification of radiographic images for COVID-19 diagnosis (2023)
Weiguang, Liu; Rodrigues, Rafael Delalibera ; Jianglong, Yan; Yu-Tao, Zhu; Pereira, Everson José de Freitas; Gen, Li; Qiusheng, Zheng; Liang, Zhao
Source: PLOS ONE. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE, RADIOGRAFIA, COVID-19
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ABNT
WEIGUANG, Liu et al. Complex network-based classification of radiographic images for COVID-19 diagnosis. PLOS ONE, v. 18, n. 9, p. 1-26, 2023Tradução . . Disponível em: https://doi.org/10.1371/journal.pone.0290968. Acesso em: 30 ago. 2024.
APA
Weiguang, L., Rodrigues, R. D., Jianglong, Y., Yu-Tao, Z., Pereira, E. J. de F., Gen, L., et al. (2023). Complex network-based classification of radiographic images for COVID-19 diagnosis. PLOS ONE, 18( 9), 1-26. doi:10.1371/ journal.pone.0290968
NLM
Weiguang L, Rodrigues RD, Jianglong Y, Yu-Tao Z, Pereira EJ de F, Gen L, Qiusheng Z, Liang Z. Complex network-based classification of radiographic images for COVID-19 diagnosis [Internet]. PLOS ONE. 2023 ; 18( 9): 1-26.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1371/journal.pone.0290968
Vancouver
Weiguang L, Rodrigues RD, Jianglong Y, Yu-Tao Z, Pereira EJ de F, Gen L, Qiusheng Z, Liang Z. Complex network-based classification of radiographic images for COVID-19 diagnosis [Internet]. PLOS ONE. 2023 ; 18( 9): 1-26.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1371/journal.pone.0290968
Artigo de periodico
Multilevel coarsening for interactive visualization of large bipartite networks (2022)
Valejo, Alan Demetrius Baria ; Fabbri, Renato ; Lopes, Alneu de Andrade ; Liang, Zhao; Oliveira, Maria Cristina Ferreira de
Source: Frontiers in Research Metrics and Analytics. Unidades: ICMC, FFCLRP
Subjects: REDES COMPLEXAS, TEORIA DOS GRAFOS, VISUALIZAÇÃO
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ABNT
VALEJO, Alan Demetrius Baria et al. Multilevel coarsening for interactive visualization of large bipartite networks. Frontiers in Research Metrics and Analytics, v. 7, p. 1-18, 2022Tradução . . Disponível em: https://doi.org/10.3389/frma.2022.855165. Acesso em: 30 ago. 2024.
APA
Valejo, A. D. B., Fabbri, R., Lopes, A. de A., Liang, Z., & Oliveira, M. C. F. de. (2022). Multilevel coarsening for interactive visualization of large bipartite networks. Frontiers in Research Metrics and Analytics, 7, 1-18. doi:10.3389/frma.2022.855165
NLM
Valejo ADB, Fabbri R, Lopes A de A, Liang Z, Oliveira MCF de. Multilevel coarsening for interactive visualization of large bipartite networks [Internet]. Frontiers in Research Metrics and Analytics. 2022 ; 7 1-18.[citado 2024 ago. 30 ] Available from: https://doi.org/10.3389/frma.2022.855165
Vancouver
Valejo ADB, Fabbri R, Lopes A de A, Liang Z, Oliveira MCF de. Multilevel coarsening for interactive visualization of large bipartite networks [Internet]. Frontiers in Research Metrics and Analytics. 2022 ; 7 1-18.[citado 2024 ago. 30 ] Available from: https://doi.org/10.3389/frma.2022.855165
Artigo de periodico
Network community detection via iterative edge removal in a flocking-like system (2021)
Verri, Filipe Alves Neto ; Gueleri, Roberto Alves ; Qiusheng, Zheng; Junbao, Zhang; Liang, Zhao
Source: European Physical Journal - Special Topics. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, SISTEMAS DINÂMICOS, ALGORITMOS ÚTEIS E ESPECÍFICOS
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ABNT
VERRI, Filipe Alves Neto et al. Network community detection via iterative edge removal in a flocking-like system. European Physical Journal - Special Topics, v. 230, n. 14-15, p. 2843-2855, 2021Tradução . . Disponível em: https://doi.org/10.1140/epjs/s11734-021-00154-5. Acesso em: 30 ago. 2024.
APA
Verri, F. A. N., Gueleri, R. A., Qiusheng, Z., Junbao, Z., & Liang, Z. (2021). Network community detection via iterative edge removal in a flocking-like system. European Physical Journal - Special Topics, 230( 14-15), 2843-2855. doi:10.1140/epjs/s11734-021-00154-5
NLM
Verri FAN, Gueleri RA, Qiusheng Z, Junbao Z, Liang Z. Network community detection via iterative edge removal in a flocking-like system [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2843-2855.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00154-5
Vancouver
Verri FAN, Gueleri RA, Qiusheng Z, Junbao Z, Liang Z. Network community detection via iterative edge removal in a flocking-like system [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2843-2855.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00154-5
Artigo de periodico
Stock market trend detection and automatic decision-making through a network-based classification model (2021)
Colliri, Tiago Santos ; Liang, Zhao
Source: Natural Computing. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, BOLSA DE VALORES
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ABNT
COLLIRI, Tiago Santos e LIANG, Zhao. Stock market trend detection and automatic decision-making through a network-based classification model. Natural Computing, v. 20, n. 4, p. 791-804, 2021Tradução . . Disponível em: https://doi.org/10.1007/s11047-020-09829-9. Acesso em: 30 ago. 2024.
APA
Colliri, T. S., & Liang, Z. (2021). Stock market trend detection and automatic decision-making through a network-based classification model. Natural Computing, 20( 4), 791-804. doi:10.1007/s11047-020-09829-9
NLM
Colliri TS, Liang Z. Stock market trend detection and automatic decision-making through a network-based classification model [Internet]. Natural Computing. 2021 ; 20( 4): 791-804.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1007/s11047-020-09829-9
Vancouver
Colliri TS, Liang Z. Stock market trend detection and automatic decision-making through a network-based classification model [Internet]. Natural Computing. 2021 ; 20( 4): 791-804.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1007/s11047-020-09829-9
Tese (Doutorado)
Network-based high level classification: novel models and applications (2021)
Colliri, Tiago Santos; Liang, Zhao (Orientador)
Unidade: ICMC
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, COVID-19
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLLIRI, Tiago Santos. Network-based high level classification: novel models and applications. 2021. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2021. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26032021-102400/. Acesso em: 30 ago. 2024.
APA
Colliri, T. S. (2021). Network-based high level classification: novel models and applications (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26032021-102400/
NLM
Colliri TS. Network-based high level classification: novel models and applications [Internet]. 2021 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26032021-102400/
Vancouver
Colliri TS. Network-based high level classification: novel models and applications [Internet]. 2021 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26032021-102400/
Trabalho de evento
An optimized modularity-based high level classification model (2020)
Colliri, Tiago Santos ; Weiguang, Liu; Liang, Zhao
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, RECONHECIMENTO DE PADRÕES
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ABNT
COLLIRI, Tiago Santos e WEIGUANG, Liu e LIANG, Zhao. An optimized modularity-based high level classification model. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9206755. Acesso em: 30 ago. 2024.
APA
Colliri, T. S., Weiguang, L., & Liang, Z. (2020). An optimized modularity-based high level classification model. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN48605.2020.9206755
NLM
Colliri TS, Weiguang L, Liang Z. An optimized modularity-based high level classification model [Internet]. Proceedings. 2020 ;[citado 2024 ago. 30 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206755
Vancouver
Colliri TS, Weiguang L, Liang Z. An optimized modularity-based high level classification model [Internet]. Proceedings. 2020 ;[citado 2024 ago. 30 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206755
Artigo de periodico
A tourist walk approach for internal and external outlier detection (2020)
Rodrigues, Rafael Delalibera ; Liang, Zhao ; Zheng, Qiusheng; Zhang, Junbao
Source: Neurocomputing. Unidades: FFCLRP, ICMC
Subjects: TURISMO, MEMÓRIA (ELETRÔNICA DIGITAL), ATRATORES
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RODRIGUES, Rafael Delalibera et al. A tourist walk approach for internal and external outlier detection. Neurocomputing, v. 393, p. 203-213, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2018.10.113. Acesso em: 30 ago. 2024.
APA
Rodrigues, R. D., Liang, Z., Zheng, Q., & Zhang, J. (2020). A tourist walk approach for internal and external outlier detection. Neurocomputing, 393, 203-213. doi:10.1016/j.neucom.2018.10.113
NLM
Rodrigues RD, Liang Z, Zheng Q, Zhang J. A tourist walk approach for internal and external outlier detection [Internet]. Neurocomputing. 2020 ; 393 203-213.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1016/j.neucom.2018.10.113
Vancouver
Rodrigues RD, Liang Z, Zheng Q, Zhang J. A tourist walk approach for internal and external outlier detection [Internet]. Neurocomputing. 2020 ; 393 203-213.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1016/j.neucom.2018.10.113
Dissertação (Mestrado)
A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks (2020)
Martins, Luan Vinicius de Carvalho; Liang, Zhao (Orientador)
Unidade: ICMC
Subjects: REDES COMPLEXAS, DINÂMICA ESTOCÁSTICA, COMPUTAÇÃO BIOINSPIRADA
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ABNT
MARTINS, Luan Vinicius de Carvalho. A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks. 2020. Dissertação (Mestrado) – Universidade de São Paulo, São Carlos, 2020. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20082020-101929/. Acesso em: 30 ago. 2024.
APA
Martins, L. V. de C. (2020). A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks (Dissertação (Mestrado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20082020-101929/
NLM
Martins LV de C. A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks [Internet]. 2020 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20082020-101929/
Vancouver
Martins LV de C. A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks [Internet]. 2020 ;[citado 2024 ago. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20082020-101929/
Trabalho de evento-anais periodico
Particle competition for unbalanced community detection in complex networks (2020)
Martins, Luan Vinicius Carvalho; Liang, Zhao
Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: FFCLRP, ICMC
Assunto: REDES COMPLEXAS
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ABNT
MARTINS, Luan Vinicius Carvalho e LIANG, Zhao. Particle competition for unbalanced community detection in complex networks. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_22. Acesso em: 30 ago. 2024. , 2020
APA
Martins, L. V. C., & Liang, Z. (2020). Particle competition for unbalanced community detection in complex networks. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_22
NLM
Martins LVC, Liang Z. Particle competition for unbalanced community detection in complex networks [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 322-336.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_22
Vancouver
Martins LVC, Liang Z. Particle competition for unbalanced community detection in complex networks [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 322-336.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_22
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: 30 ago. 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 ago. 30 ] 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 ago. 30 ] Available from: https://doi.org/10.1038/s41598-019-57123-1
Trabalho de evento
Echo state network performance analysis using non-random topologies (2020)
Arroyo, Diana Carolina Roca ; Florez, Alexander Ylnner Choquenaira ; Flores, Daniela Milón ; Romero, Roseli Aparecida Francelin ; Liang, Zhao
Source: Proceedings. Conference titles: IEEE Colombian Conference on Applications of Computational Intelligence - ColCACI. Unidades: ICMC, FFCLRP
Subjects: SISTEMAS DINÂMICOS, ANÁLISE DE SÉRIES TEMPORAIS, REDES COMPLEXAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ARROYO, Diana Carolina Roca et al. Echo state network performance analysis using non-random topologies. 2020, Anais.. Danvers: IEEE, 2020. Disponível em: https://doi.org/10.1109/ColCACI50549.2020.9248714. Acesso em: 30 ago. 2024.
APA
Arroyo, D. C. R., Florez, A. Y. C., Flores, D. M., Romero, R. A. F., & Liang, Z. (2020). Echo state network performance analysis using non-random topologies. In Proceedings. Danvers: IEEE. doi:10.1109/ColCACI50549.2020.9248714
NLM
Arroyo DCR, Florez AYC, Flores DM, Romero RAF, Liang Z. Echo state network performance analysis using non-random topologies [Internet]. Proceedings. 2020 ;[citado 2024 ago. 30 ] Available from: https://doi.org/10.1109/ColCACI50549.2020.9248714
Vancouver
Arroyo DCR, Florez AYC, Flores DM, Romero RAF, Liang Z. Echo state network performance analysis using non-random topologies [Internet]. Proceedings. 2020 ;[citado 2024 ago. 30 ] Available from: https://doi.org/10.1109/ColCACI50549.2020.9248714
Trabalho de evento-anais periodico
Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network (2020)
Colliri, Tiago Santos ; Delbem, Alexandre Cláudio Botazzo ; Liang, Zhao
Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: ICMC, FFCLRP
Subjects: ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO, COVID-19, REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLLIRI, Tiago Santos e DELBEM, Alexandre Cláudio Botazzo e LIANG, Zhao. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_27. Acesso em: 30 ago. 2024. , 2020
APA
Colliri, T. S., Delbem, A. C. B., & Liang, Z. (2020). Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_27
NLM
Colliri TS, Delbem ACB, Liang Z. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 397-411.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_27
Vancouver
Colliri TS, Delbem ACB, Liang Z. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 397-411.[citado 2024 ago. 30 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_27

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