Jämför metoder

	Grafuppmärksamhetsnätverk	Random Forest	Återkommande neuralt nätverk
Ämnesområde	Djupinlärning	Maskininlärning	Djupinlärning
Familj	Machine learning	Machine learning	Machine learning
Ursprungsår	2018	2001	1986–1990
Upphovsperson	Veličković, P. et al.	Breiman, L.	Rumelhart, D. E.; Elman, J. L.
Typ	Graph neural network (attention-based)	Ensemble (bagging of decision trees)	Sequential neural network
Ursprungskälla	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Alias	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	RNN, Elman network, Jordan network, simple recurrent network
Närliggande	4	4	3
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).	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.