Vertaile menetelmiä

	Graafiverkko (Graph Attention Network, GAT)	Logistinen regressio	Rekurrentti neuroverkko
Tieteenala	Syväoppiminen	Tutkimuksen tilastomenetelmät	Syväoppiminen
Menetelmäperhe	Machine learning	Process / pipeline	Machine learning
Syntyvuosi	2018	1958	1986–1990
Kehittäjä	Veličković, P. et al.	David Roxbee Cox	Rumelhart, D. E.; Elman, J. L.
Tyyppi	Graph neural network (attention-based)	Method	Sequential neural network
Alkuperäislähde	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Rinnakkaisnimet	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	logit model, binomial logistic regression, LR	RNN, Elman network, Jordan network, simple recurrent network
Liittyvät	4	3	3
Tiivistelmä	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.	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.