Σύγκριση μεθόδων

	Δίκτυο Προσοχής Γραφήματος	Λογιστική Παλινδρόμηση
Πεδίο	Βαθιά Μάθηση	Ερευνητική Στατιστική
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	2018	1958
Δημιουργός	Veličković, P. et al.	David Roxbee Cox
Τύπος	Graph neural network (attention-based)	Method
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	logit model, binomial logistic regression, LR
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).	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.