Evaluating the use of knowledge graph embeddings to identify influential nodes in heterogeneous networks (2026)
- Authors:
- USP affiliated authors: LOPES, ALNEU DE ANDRADE - ICMC ; BARBIRATO, JOÃO GABRIEL MELO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-032-15987-8_35
- Subjects: REDES NEURAIS; TEORIA DOS GRAFOS
- Keywords: Influential Node Identification; Knowledge Graph Represen tation; HeterogeneousGraph
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 16179, p. 524-538, 2026
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
BARBIRATO, João Gabriel Melo e LOPES, Alneu de Andrade. Evaluating the use of knowledge graph embeddings to identify influential nodes in heterogeneous networks. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-032-15987-8_35. Acesso em: 20 fev. 2026. , 2026 -
APA
Barbirato, J. G. M., & Lopes, A. de A. (2026). Evaluating the use of knowledge graph embeddings to identify influential nodes in heterogeneous networks. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-032-15987-8_35 -
NLM
Barbirato JGM, Lopes A de A. Evaluating the use of knowledge graph embeddings to identify influential nodes in heterogeneous networks [Internet]. Lecture Notes in Artificial Intelligence. 2026 ; 16179 524-538.[citado 2026 fev. 20 ] Available from: https://doi.org/10.1007/978-3-032-15987-8_35 -
Vancouver
Barbirato JGM, Lopes A de A. Evaluating the use of knowledge graph embeddings to identify influential nodes in heterogeneous networks [Internet]. Lecture Notes in Artificial Intelligence. 2026 ; 16179 524-538.[citado 2026 fev. 20 ] Available from: https://doi.org/10.1007/978-3-032-15987-8_35 - A multi-view approach for semi-supervised scientific paper classification
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Informações sobre o DOI: 10.1007/978-3-032-15987-8_35 (Fonte: oaDOI API)
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