قارن الطرق

	شبكة الانتباه الرسومية	الشبكة العصبية المتكررة
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2018	1986–1990
صاحب الطريقة	Veličković, P. et al.	Rumelhart, D. E.; Elman, J. L.
النوع	Graph neural network (attention-based)	Sequential neural network
المصدر التأسيسي	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
الأسماء البديلة	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	RNN, Elman network, Jordan network, simple recurrent network
ذات صلة	4	3
الملخص	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.