Salīdzināt metodes

	Stohastiskais bloku modelis	DBSCAN	Grafu neironu tīkls
Nozare	Tīklu analīze	Mašīnmācīšanās	Dziļā mācīšanās
Saime	Process / pipeline	Machine learning	Machine learning
Izcelsmes gads	1983	1996	2017
Autors	—	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Kipf, T.N. & Welling, M.
Tips	Probabilistic generative graph model	Density-based clustering algorithm	Deep learning on graph-structured data
Pirmavots	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.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.
Citi nosaukumi	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network
Saistītās	7	3	4
Kopsavilkums	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.	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.