Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Rețea Neuronală pe Grafuri× | Clustering Ierarhic× | |
|---|---|---|
| Domeniu≠ | Învățare profundă | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2017 | 1963 |
| Autorul original≠ | Kipf, T.N. & Welling, M. | Ward, J. H. |
| Tip≠ | Deep learning on graph-structured data | Unsupervised clustering (agglomerative) |
| Sursa seminală≠ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR. link ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Denumiri alternative≠ | Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Înrudite | 4 | 4 |
| Rezumat≠ | 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. | Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963. |
| ScholarGateSet de date ↗ |
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