השוואת שיטות

	מודל הבלוקים הסטוכסטי (SBM)	DBSCAN	רשת קשב גרפית
תחום	ניתוח רשתות	למידת מכונה	למידה עמוקה
משפחה	Process / pipeline	Machine learning	Machine learning
שנת המקור	1983	1996	2018
הוגה השיטה	—	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Veličković, P. et al.
סוג	Probabilistic generative graph model	Density-based clustering algorithm	Graph neural network (attention-based)
מקור מכונן	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
כינויים	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
קשורות	7	3	4
תקציר	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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).