Σύγκριση μεθόδων

	Μοντέλο Στοχαστικών Τμημάτων	Δίκτυο Προσοχής Γραφήματος
Πεδίο	Ανάλυση Δικτύων	Βαθιά Μάθηση
Οικογένεια	Process / pipeline	Machine learning
Έτος προέλευσης	1983	2018
Δημιουργός	—	Veličković, P. et al.
Τύπος	Probabilistic generative graph model	Graph neural network (attention-based)
Θεμελιώδης πηγή	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
Εναλλακτικές ονομασίες	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Συναφείς	7	4
Σύνοψη	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.	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).