Extreme learning machine to graph convolutional networks (2022)
- Authors:
- USP affiliated authors: RODRIGUES, FRANCISCO APARECIDO - ICMC ; GONÇALVES, THALES DE OLIVEIRA - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-031-21689-3_42
- Subjects: APRENDIZADO COMPUTACIONAL; REDES COMPLEXAS; ALGORITMOS
- Keywords: Graph convolutional network; Extreme learning machine; Graph machine learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 13654, p. 601-615, 2022
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
GONÇALVES, Thales e RODRIGUES, Francisco Aparecido. Extreme learning machine to graph convolutional networks. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-21689-3_42. Acesso em: 10 fev. 2026. , 2022 -
APA
Gonçalves, T., & Rodrigues, F. A. (2022). Extreme learning machine to graph convolutional networks. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-21689-3_42 -
NLM
Gonçalves T, Rodrigues FA. Extreme learning machine to graph convolutional networks [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13654 601-615.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1007/978-3-031-21689-3_42 -
Vancouver
Gonçalves T, Rodrigues FA. Extreme learning machine to graph convolutional networks [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13654 601-615.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1007/978-3-031-21689-3_42 - Graph Neural Networks contributions and advancements
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- On degree–degree correlations in multilayer networks
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- Tweaking synchronization by connectivity modifications
- Low-dimensional behavior of Kuramoto model with inertia in complex networks
Informações sobre o DOI: 10.1007/978-3-031-21689-3_42 (Fonte: oaDOI API)
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