Comparer des méthodes

	Réseau d'attention sur graphe	Regroupement hiérarchique
Domaine	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2018	1963
Auteur d'origine	Veličković, P. et al.	Ward, J. H.
Type	Graph neural network (attention-based)	Unsupervised clustering (agglomerative)
Source fondatrice	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Alias	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Apparentées	4	4
Résumé	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).	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.