Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Red de Atención Gráfica× | Redes Neuronales de Grafos× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2018 | 2017 |
| Autor original≠ | Veličković, P. et al. | Kipf, T.N. & Welling, M. |
| Tipo≠ | Graph neural network (attention-based) | Deep learning on graph-structured data |
| Fuente seminal≠ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR. link ↗ |
| Alias | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network |
| Relacionados | 4 | 4 |
| Resumen≠ | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). | 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. |
| ScholarGateConjunto de datos ↗ |
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