เปรียบเทียบวิธี

	Graph Attention Network	โครงข่ายประสาทเทียมแบบเวียนซ้ำ (Recurrent Neural Network - RNN)
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2018	1986–1990
ผู้ริเริ่ม	Veličković, P. et al.	Rumelhart, D. E.; Elman, J. L.
ประเภท	Graph neural network (attention-based)	Sequential neural network
แหล่งต้นตำรับ	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
ชื่อเรียกอื่น	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	RNN, Elman network, Jordan network, simple recurrent network
ที่เกี่ยวข้อง	4	3
สรุป	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.