विधियों की तुलना करें

	स्टोकेस्टिक ब्लॉक मॉडल	DBSCAN	ग्राफ अटेंशन नेटवर्क	पदानुक्रमिक समूहन
क्षेत्र	नेटवर्क विश्लेषण	मशीन अधिगम	गहन अधिगम	मशीन अधिगम
परिवार	Process / pipeline	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	1983	1996	2018	1963
प्रवर्तक	—	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Veličković, P. et al.	Ward, J. H.
प्रकार	Probabilistic generative graph model	Density-based clustering algorithm	Graph neural network (attention-based)	Unsupervised clustering (agglomerative)
मौलिक स्रोत	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.
उपनाम	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
संबंधित	7	3	4	4
सारांश	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.