Characterizing data patterns with core-periphery network modeling (2023)
- Authors:
- USP affiliated authors: LIANG, ZHAO - FFCLRP ; ANGHINONI, LEANDRO - ICMC
- Unidades: FFCLRP; ICMC
- DOI: 10.1016/j.jocs.2022.101912
- Subjects: REDES COMPLEXAS; RECONHECIMENTO DE IMAGEM; RADIOGRAFIA; COVID-19
- Keywords: Data classification; Core-periphery network; Dispersed class pattern
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Computational Science
- ISSN: 1877-7503
- Volume/Número/Paginação/Ano: v. 66, p. 1-13, Jan. 2023
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
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: 25 fev. 2026. -
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 2026 fev. 25 ] 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 2026 fev. 25 ] Available from: https://doi.org/10.1016/j.jocs.2022.101912 - TransGNN: a transductive graph neural network with graph dynamic embedding
- Time series trend detection and forecasting using complex network topology analysis
- Time series pattern identification by hierarchical community detection
- Temporal network pattern identification by community modelling
- Classificação e previsão de séries temporais através de redes complexas
- Structure characterization of complex networks for machine learning
- Analysis of the effectiveness of public health measures on COVID-19 transmission
- Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data
- Computer-aided music composition with LSTM neural network and chaotic inspiration
- Semi-supervised learning by edge domination in complex networks
Informações sobre o DOI: 10.1016/j.jocs.2022.101912 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
| 3114272.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
