Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Rede Neural de Grafos× | XGBoost× | |
|---|---|---|
| Área≠ | Aprendizado profundo | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2017 | 2016 |
| Autor original≠ | Kipf, T.N. & Welling, M. | Chen, T. & Guestrin, C. |
| Tipo≠ | Deep learning on graph-structured data | Ensemble (gradient-boosted decision trees) |
| Fonte seminal≠ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Outros nomes≠ | Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network | XGBoost, extreme gradient boosting, scalable tree boosting |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateConjunto de dados ↗ |
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