Σύγκριση μεθόδων

	Δίκτυο Προσοχής Γραφήματος	Αναδρομικό Νευρωνικό Δίκτυο
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2018	1986–1990
Δημιουργός	Veličković, P. et al.	Rumelhart, D. E.; Elman, J. L.
Τύπος	Graph neural network (attention-based)	Sequential neural network
Θεμελιώδης πηγή	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Εναλλακτικές ονομασίες	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	RNN, Elman network, Jordan network, simple recurrent network
Συναφείς	4	3
Σύνοψη	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 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.