M-Flash: fast billion-scale graph computation using a bimodal block processing model (2016)
- Authors:
- USP affiliated authors: CORDEIRO, ROBSON LEONARDO FERREIRA - ICMC ; RODRIGUES JUNIOR, JOSÉ FERNANDO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-319-46227-1_39
- Subjects: COMPUTAÇÃO GRÁFICA; REDES COMPLEXAS; MINERAÇÃO DE DADOS; ALGORITMOS GRÁFICOS
- Keywords: Graph processing; Graph mining
- Language: Inglês
- Imprenta:
- Source:
- Título: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 9852, p. 623-640, 2016
- Conference titles: European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
GUALDRON, Hugo et al. M-Flash: fast billion-scale graph computation using a bimodal block processing model. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-46227-1_39. Acesso em: 27 dez. 2025. , 2016 -
APA
Gualdron, H., Cordeiro, R. L. F., Rodrigues Junior, J. F., Chau, D. H. (P. ), Kahng, M., & Kang, U. (2016). M-Flash: fast billion-scale graph computation using a bimodal block processing model. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-319-46227-1_39 -
NLM
Gualdron H, Cordeiro RLF, Rodrigues Junior JF, Chau DH (P), Kahng M, Kang U. M-Flash: fast billion-scale graph computation using a bimodal block processing model [Internet]. Lecture Notes in Artificial Intelligence. 2016 ; 9852 623-640.[citado 2025 dez. 27 ] Available from: https://doi.org/10.1007/978-3-319-46227-1_39 -
Vancouver
Gualdron H, Cordeiro RLF, Rodrigues Junior JF, Chau DH (P), Kahng M, Kang U. M-Flash: fast billion-scale graph computation using a bimodal block processing model [Internet]. Lecture Notes in Artificial Intelligence. 2016 ; 9852 623-640.[citado 2025 dez. 27 ] Available from: https://doi.org/10.1007/978-3-319-46227-1_39 - Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations
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Informações sobre o DOI: 10.1007/978-3-319-46227-1_39 (Fonte: oaDOI API)
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