Efficient Laplacian spectral density computations for networks with arbitrary degree distributions (2021)
- Authors:
- USP affiliated authors: FUJITA, ANDRÉ - IME ; GUZMÁN, GROVER ENRIQUE CASTRO - IME
- Unidade: IME
- DOI: 10.1017/nws.2021.10
- Assunto: TEORIA DOS GRAFOS
- Keywords: spectral density; Laplacian matrix; normalized Laplacian matrix; configuration model tree-like network
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Network Science
- ISSN: 2050-1242
- Volume/Número/Paginação/Ano: v. 9, n. 3, p. 312-327, 2021
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
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: 27 dez. 2025. -
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 2025 dez. 27 ] 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 2025 dez. 27 ] Available from: https://doi.org/10.1017/nws.2021.10 - Convolution-based linear discriminant analysis for functional data classification
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Informações sobre o DOI: 10.1017/nws.2021.10 (Fonte: oaDOI API)
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