Comparer des méthodes

	Réseau d'attention sur graphe	XGBoost
Domaine	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2018	2016
Auteur d'origine	Veličković, P. et al.	Chen, T. & Guestrin, C.
Type	Graph neural network (attention-based)	Ensemble (gradient-boosted decision trees)
Source fondatrice	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	4	5
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).	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.