Salīdzināt metodes

	Stohastiskais bloku modelis	DBSCAN	Grafu uzmanības tīkls (Graph Attention Network, GAT)	Hierarhiskā klasterizācija
Nozare	Tīklu analīze	Mašīnmācīšanās	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Process / pipeline	Machine learning	Machine learning	Machine learning
Izcelsmes gads	1983	1996	2018	1963
Autors	—	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Veličković, P. et al.	Ward, J. H.
Tips	Probabilistic generative graph model	Density-based clustering algorithm	Graph neural network (attention-based)	Unsupervised clustering (agglomerative)
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.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Citi nosaukumi	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	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Saistītās	7	3	4	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.	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).	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.