เปรียบเทียบวิธี

	Graph Attention Network	การถดถอยโลจิสติก
สาขาวิชา	การเรียนรู้เชิงลึก	สถิติการวิจัย
ตระกูล	Machine learning	Process / pipeline
ปีกำเนิด	2018	1958
ผู้ริเริ่ม	Veličković, P. et al.	David Roxbee Cox
ประเภท	Graph neural network (attention-based)	Method
แหล่งต้นตำรับ	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
ชื่อเรียกอื่น	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	logit model, binomial logistic regression, LR
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).	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.