Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Grafuppmärksamhetsnätverk× | Grafneuralnätverk× | Hierarkisk klustring× | |
|---|---|---|---|
| Ämnesområde≠ | Djupinlärning | Djupinlärning | Maskininlärning |
| Familj | Machine learning | Machine learning | Machine learning |
| Ursprungsår≠ | 2018 | 2017 | 1963 |
| Upphovsperson≠ | Veličković, P. et al. | Kipf, T.N. & Welling, M. | Ward, J. H. |
| Typ≠ | Graph neural network (attention-based) | Deep learning on graph-structured data | Unsupervised clustering (agglomerative) |
| Ursprungskälla≠ | 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 ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| 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 | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Närliggande | 4 | 4 | 4 |
| Sammanfattning≠ | 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. | 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. |
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