Recovering network topology and dynamics from sequences: a machine learning approach (2024)
- Authors:
- USP affiliated authors: AMANCIO, DIEGO RAPHAEL - ICMC ; GUERREIRO, LUCAS - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.physa.2024.129618
- Subjects: APRENDIZADO COMPUTACIONAL; REDES COMPLEXAS; HEURÍSTICA
- Keywords: Random walks; Supervised classifiers; Link prediction
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Physica A : statistical mechanics and its applications
- ISSN: 0378-4371
- Volume/Número/Paginação/Ano: v. 638, p. 1-13, Mar. 2024
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
GUERREIRO, Lucas e SILVA, Filipi Nascimento e AMANCIO, Diego Raphael. Recovering network topology and dynamics from sequences: a machine learning approach. Physica A : statistical mechanics and its applications, v. 638, p. 1-13, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.physa.2024.129618. Acesso em: 20 fev. 2026. -
APA
Guerreiro, L., Silva, F. N., & Amancio, D. R. (2024). Recovering network topology and dynamics from sequences: a machine learning approach. Physica A : statistical mechanics and its applications, 638, 1-13. doi:10.1016/j.physa.2024.129618 -
NLM
Guerreiro L, Silva FN, Amancio DR. Recovering network topology and dynamics from sequences: a machine learning approach [Internet]. Physica A : statistical mechanics and its applications. 2024 ; 638 1-13.[citado 2026 fev. 20 ] Available from: https://doi.org/10.1016/j.physa.2024.129618 -
Vancouver
Guerreiro L, Silva FN, Amancio DR. Recovering network topology and dynamics from sequences: a machine learning approach [Internet]. Physica A : statistical mechanics and its applications. 2024 ; 638 1-13.[citado 2026 fev. 20 ] Available from: https://doi.org/10.1016/j.physa.2024.129618 - Identifying the perceived local properties of networks reconstructed from biased random walks
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Informações sobre o DOI: 10.1016/j.physa.2024.129618 (Fonte: oaDOI API)
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